Issue 91 - September 2021
The Journal of the Society of Technical Analysts
STA LIBRARY GOES DIGITAL 09
The mystery of HODL - why Bitcoin deserves active management
Exploration of a trading strategy system
A three-point system to find highprobability assets
Interview with Deborah Owen FSTA
Charlie Morris, MSTA
Petri Nousiainen, CEFA
Nicole Elliott, FSTA
FOREWORD Editor's letter 03 Advertisement: IFTA Conference online conference 04 Advertisement: Energy Trdaing Week 05 NEWS Technical indicators that make sense - remembering J Welles Wilder 06 Gerry Celaya, MSTA STA Library goes digital! 08 RESEARCH The mystery of HODL - why Bitcoin deserves active management 09 Charlie Morris, MSTA Interdisciplinary price analysis 15 Tom Bundgaard, MSTA MFTA Exploration of a trading strategy system 19 Petri Nousiainen, CEFA A three-point system to find high-probability assets 22 Zaheer Anwari Elliott taken on - shape and price 26 Christopher Mack, MSTA
ANALYST FOCUS Head and shoulders above 32 Steve O’Hare, MSTA Interview with Deborah Owen FSTA 35 Nicole Elliott, FSTA
BOOK & SOFTWARE REVIEW Book review: The Mental Game of Trading by Jared Tendler 37 Alistair Philip, MSTA Bytes & Pieces 39 David Watts, MSTA
THE STA Benefits of STA Membership 40 STA Calendar 2021 / 2022 41 The Education Channel 42 STA Library 42 Patricia Elbaz catches up with Axel Rudolph 43 Special Journal Offer 45 STA Diploma Part 1 Course 46 STA Diploma Part 2 Course 47 STA Home Study Course 48 Congratulations! Latest STA Diploma MSTAs 49 STA Executive Committee 50 STA Advertising Rates 2021 51
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.
Nicole Elliott, FSTA Technical Analyst, Private Investor, E-journalist for the STA
Locked-up in limbo in London for all too long, I’ve changed - and I know I’m not alone. From being the sort of frustrated person longing to get out and about again, I’ve come to accept my enforced incarceration, while some of my friends have actually embraced it and don’t want to do anything too new or different or farfetched. Agoraphobia here we come! Being a social psychologist, it set me thinking as to whether this shift of mine - and possibly like all too many things it might be yet another effect of ageing - could alter my investment decisions; and whether women generally are more risk-averse than men, not just now, but always. In the Analyst Focus section, in my interview with Deborah Owen (pg.35), she believes it’s not the pandemic but central bank action since the Great Financial Crisis over a decade ago that has affected market cycles and reinforced existing trends. In June’s IFTA video conference (do sign up for these or watch on demand later) UK-based Andrew Pancholi talks about very long-term cycles and in particular points out the ‘pandemic’ one. He starts with smallpox in 1681, which wiped out much of the Americas’ first people, measles in 1717, cholera in 1817, influenza in 1918 - and now Covid-19 plus horrendous
heat, drought and plagues in many countries. The summer issue of the quarterly magazine New Humanist has as its cover story the ‘conspiracy takeover’. In his article on paranoid states, David Hutt notes that: ‘academics today are busy exploring how they [conspiracy theories] are part of the universal mind, pervasive in democracies and authoritarian states alike’. Surely this too will affect investor decisions, where many are moving in silos and vacuums. But now that Boris has ‘freed’ us in England, what can we look forward to for the rest of 2021? STA members have dates for their diaries: The Freedom party bash will take place at the National Liberal Club on 23 September. Members can look forward to the return of meetings at One Moorgate Place from October, with Anthony Cheung speaking at the first one on the 12th of the month. Plus there’s the Christmas party on Tuesday, 14 December; can’t wait to see familiar faces! Petri Nousiainen, (pg.19) a Master of Engineering, submits his June 2021 thesis on machine learning to improve
trading strategy. Christopher Mack, (pg.26) also an engineering graduate (from Canterbury), writes a mercifully down-to-earth article on Elliott Wave Theory with very little jargon. Back to the Analyst Focus, Steve O’Hare (pg.32) has a colourful piece on how he has now become a disruptor. And now to books. Do see our review (pg.37) of Jared Tendler’s ‘The Mental Game of Trading’. A more cuttingedge novelty, the STA collection at the Barbican Library will, bit by bit, become available online via a new app to members. We believe this will be of great value to those who live further afield in the UK, just as the Home Study Course has been a great success not only in the UK but abroad. Finally, remember that as the Market Technician is now digital only, we are more flexible in terms of content, space and layout. Do consider submitting a piece of research, a news article, or even a letter to yours truly, the editor.
Technical Indicators That Make Sense Remembering J. Welles Wilder J. Welles Wilder passed away on 18 April 2021, leaving a legacy of changing the way that many analysts, traders and investors study and trade financial markets. I never met him, but I reached out to my first boss at Money Market Services, Cindy Armijo (née Keel) and she remembered him with fondness. Cindy recalled seeing him at MTA and TSAA meetings when he was passing through the San Francisco Bay area. He was one of her early mentors, and was a traditionalist in the sense that "read the charts" was his foundational philosophy, but he brought so much more to the table. His seminal book ‘New Concepts in Technical Trading Systems’ was published in 1978. It is not a long read - only 118 pages. However, it is also not an easy read as Welles Wilder illustrates how to calculate his indicators in great detail using tables and worksheets, which can be tedious at times.
Gerry Celaya, MSTA Gerry Celaya has been professionally involved in global market research and investments since 1986. Gerry is a director at Redtower Asset Management and Tricio Investment Advisors, providing research and risk management consultancy services in the FX and investment markets to professional clients around the world... Read more
There were books on technical and chart analysis written before 1978 and plenty of books about technical and chart analysis have been written since of course, including a plethora on ‘behavioural finance’ over the last 20 years as authors cast a wide net to make sales. But Welles Wilder’s book stands out as he comes right out and says: “I need a system to trade the markets, I need a system to tell me if I am trading a trending or non-trending market, and I need a money management system”. His book details the different indicators and systems that should be used when markets are trending or not trending (and how to determine the different states of trend). Analysts, traders and investors may often use the technical indicators and systems that he invented, without even knowing it. As the reader goes through the book, one of the final chapters details his ‘Commodity Selection Index’. Here the author gets into leverage cost and volatility, and ties in some of his other indicators in order to attempt to focus the trader on the markets that it makes sense to trade at that time. As he states:
“His book details the different indicators and systems that should be used when markets are trending or not trending (and how to determine the different states of trend).” Gerry Celaya, MSTA
“Most technical systems are trend-following systems; however, most commodities are in a good trending mode (high directional movement) only about 30% of the time. If the trader follows the same commodities or stocks all of the time, then his system has to be good enough to make more money 30% of the time than it will give back 70% of the time.” The most famous of his indicators introduced in this book is the Relative Strength Index (RSI), which every Bloomberg and Reuters terminal chart uses, along with all of the free (and paid) internet charting /broking systems. Having programmed Welles Wilder’s indicators into mainframe computers in the 1980s, moving onto Lotus 123 (remember that?) and then Excel spreadsheets, writing analysis, proprietary trading at banks and helping to run funds and investments in all asset
NEWS classes, I have learned that it takes discipline to use his indicators and systems. For example, the RSI seems simple, but this is not just an ‘overbought/oversold’ indicator. There are a lot of nuances (which Welles Wilder goes through) in using the RSI that are important to understand when you are using it. Figure 1-4 below show various indicators that Welles Wilder created, underlining how much we owe to his creative and quantitative efforts. Figure 1: Welles Wilder RSI (TradingView)
Figure 2: Welles Wilder Parabolic SAR (TradingView)
Figure 3: Welles Wilder Volatility (TradingView)
Figure 4: Welles Wilder DMI (TradingView)
Table 1: The more you lose, the harder it is to make it back
A table that appears near the end of the book in the ‘Capital Management’ chapter is important for any analyst, trader and investor to understand (my version is Table 1).
Initial capital lost
Required gains to recover
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90%
5.3% 11.1% 17.6% 25.0% 33.3% 42.9% 53.8% 66.7% 81.8% 100% 122% 150% 186% 233% 300% 400% 567% 900%
There is a cost to losing money, and the more of your capital that you lose, the bigger the hill to climb to make it back. A 20% loss of your initial capital requires a 33.33% return to make up, for example, while a 90% loss of your initial capital requires a 900% return to make it up. A simple lesson, and common sense, but often forgotten it seems. Thank you, Welles Wilder.
STA Library goes digital! The STA are delighted that STA UK members are able to access the City of London Barbican Library’s digital Overdrive collection via their Libby App. Library members can choose from a great selection of eBooks, eAudiobooks, eMagazines, eComics and music videos which can be downloaded on to many devices including tablets, mobile phone and many devices compatible with the Libby App. As before, UK STA members wishing to become a member of the City of London libraries do this by going into the Barbican Library or another one of their libraries to join with proof of home address and ID, or you can apply for temporary online membership which will currently give access to Overdrive and Libby. If your library card has expired then you will need to get it reinstated before you will be able to access Overdrive / Libby. Once you have a valid membership number you can access the collection by downloading the Libby / Overdrive app to your device, search for City of London Libraries and then input your library membership number. Over the coming months the STA and the Barbican Library are working together to add a selection of technical analysis. Meantime, UK members an explore the collection of books, magazines via https://cityoflondonuk.overdrive.com
The mystery of HODL - why Bitcoin deserves active management Introduction Bitcoin gurus love lofty price targets. They believe that because Bitcoin has a finite supply, its ultimate success is assured. “It’s in the math”. As people scramble to buy the last Bitcoins, the price will go to the moon. There can be no question that limited supply beats unlimited supply as the latter makes price appreciation difficult. If in doubt, take a loot at the FIAT currencies, which fall in real terms over the years. But what the supply side gurus seem to forget is that the future supply is laid out in front of us and is therefore known. How can something widely known possibly add value in financial markets? It can’t. Charlie Morris ByteTree Asset Management Charlie Morris is the chief investment officer at ByteTree Asset Management. He is a lead portfolio manager and develops both crypto and traditional investment strategies. He has 23 years of experience in fund management, where he has built a reputation for managing actively managed, multi-asset portfolios, with an emphasis on efficient diversification and risk management. Although well versed in traditional asset classes, he is best known for his expertise in alternative assets, notably gold and Bitcoin.
Figure 1: Bitcoin has doubled every year
It’s a demand story With supply fixed and known, and therefore any benefits already reflected in the price, Bitcoin is a demand story. Demand is driven by adoption, and as with all financial booms and busts, it comes in waves. The crowd go nuts for Bitcoin, but after a few months, the hype cycle cools and investors flock. Yet so far at least, each cycle has surpassed the last. The result is that Bitcoin is a cyclical asset class, with enormous bull and bear markets of a scale that we just don’t see in stock markets or commodities. Looking at a log regression from the 2013 peak ($1,137) to date (Figure 1), and ignoring the early days where the price really, really motored, we get a convenient 100% trend. It’s really simple: Bitcoin has doubled every year, if you are allowed to smooth over the cracks. And today, it trades near the middle of the trend channel.
RESEARCH The bull market of 2015-2017 saw a 9,060% rise, and the recent bull 1,704% from the 2018 low to the recent high. The bears were a brutal -81% in 2014 and -83% in 2018. This is crazy stuff. If you take a cyclical sector, such as steel or shipping, you get some good rallies from time to time followed by prolonged bear markets. But these cycles come around three or four times over the course of your career. In crypto, the wild swings keep on coming, making it a great space for traders. Bitcoin is a cyclical asset I started studying Bitcoin in 2013, having rejected it as a ridiculous idea back in 2011. That was a costly mistake, but at least I managed to U-turn. It’s been one hell of a learning curve ever since. Imagine an equity that carries out a capital raise every single day. Yet the capital doesn’t remain inside the company; it is paid out to contractors that manage their operations. That is what happens as the miners sell the Bitcoin they generate each day in compensation for operating the network. They validate all of the transactions - and keep Bitcoin secure. With current Bitcoin supply of around BTC27,000 per month, that means $800m is needed to support the network each month at a BTC price of $30,000 or $1.6bn at $60,000. The next halving, whereby the block reward falls from BTC6.25 to BTC3.125, is expected to take place in July 2024. This forecast moves according to the hashing power (quantity of mining power). As I write, there are 151,000 Figure 2: How can TA cope with such high volatility?
blocks to be mined before we get there, generating BTC943,750. That means over the next three years, the Bitcoin network must absorb $28bn of capital at $30,000 BTC price or $56bn at $60,000. Now imagine the implications of a $100,000 BTC price - or even $500,000 which some folk assure us is on its way? The network would need to see capital inflows of $94bn and $471bn respectively. These are huge numbers and would be entirely implausible, even with a vibrant Fed. At least for now... Given the gold market see $50bn of inflows in an exceptional year, how can Bitcoin beat that by multiples? I think this line of thinking is extremely helpful in coming up with plausible scenarios. Of course, after 2024, the block reward halves, making it easier for higher prices to be supported. It happens again in 2028, 2032 and 2036 etc. Each time, the selling pressure from the miners falls, and that paves the way for a bullish case over the long term. In 10 or 20 years, lofty price targets are entirely possible provided the Bitcoin community remains engaged and hasn’t found a new toy. I suspect the risks of Bitcoin being superseded are higher than zero but lower than probable. That’s why analysis is required. Bitcoin and technical analysis Bitcoin’s biggest problem for technical analysts is its volatility. Most conventional asset classes happily live with sub 20% or 30% volatility, and bonds sub 10%. Bitcoin ranges between 30% and 90% (Figure 2). How can a technical trader manage this? Source: Bloomberg - Bitcoin 90 day realised volatility since 2012
RESEARCH Despite what many say, volatility provides opportunity, but makes trading tough. On a 2018 to date long-only backtest on Bloomberg of simple technical trading strategies, few exceed the results from buy and hold. The triangular moving 50-day average comes out on top (Figure 3). It gets you in for the big rallies, but the lengthy consolidation periods have been costly. Figure 3: Trading Bitcoin with triangular moving averages
Source: Bloomberg - Bitcoin 90 day realised volatility since 2018
Overtrading has been the death of many traders and that’s been hell for the oscillators. Bollinger bands, as an oscillator, have lost money - as if that’s even possible in such a dynamic asset. Trading volatility breakouts, or the squeeze, would be better. Trend following Of course, trend following strategies have performed best, but as I said, the consolidation periods have been painful for moving average crossovers and so forth, especially if they are short-term. Personally, I’m a great fan of max min channels (Figure 4). I show Bitcoin over 80 days. Buy the max, sell the min. It’s touching the red. Figure 4: Bitcoin and the max min indicator
Source: Bloomberg - Bitcoin with 80-day max min since 2016
RESEARCH It’s what I would call a gentleman’s trading strategy: you pick up the bull markets and avoid the worst of the bears. If you speed it up to 30 days or less, turnover becomes high because Bitcoin is so volatile but, still, it’s a simple way to manage levels. Bitcoin and on-chain data While I’m always conscious of the technicals, I founded ByteTree.com to explore a wide range of the alternative datasets that help to build a picture of Bitcoin. As I wrote earlier, supply is known and so measuring demand is key. For this, we measure fund and ETF flows into Bitcoin, which follows the institutional allocations. In Figure 5, I show the 90-day net flows into Bitcoin funds less BTC sold by the miners selling.
Figure 5: Measuring demand is key
Source: https://bytetreeam.com/bitcoin-flows as described
Notice how, when the net flows turned positive in October, the price of Bitcoin surged. It started to fall in January as the price formed a topping pattern. It turned negative again in March 2021, prior to a peak and a 50% retracement. There can be no doubt that Bitcoin flows are an important metric to follow. Many people Bitcoin exchange flows, address balances and web traffic trends. These are all interesting to watch, but seriously flaky data points; I know that because I have tested them. Sure you’ll pick up nuggets, but a systematic strategy would fail. Institutional dominance ratio ByteTree has a second method that targets the big money and that is the Institutional Dominance Ratio, or IDR. Quite simply, it looks at the size of transactions. If the large transactions overwhelm the network, IDR is high and institutional trades dominate the network. If it is low, the institutions are absent, and retail dominates. For example, if a block has 1,000 transactions, we look at the top 200 transactions as a percentage of all transactions (Figure 6). This is how it has varied over time. The data sets are noisy, so best focus on the moving average.
RESEARCH Figure 6: Institutional interest has increased over the past decade
Source: ByteTree IDR (30-day smoothing) and price since 2011
In 2011 and 2012, the large traders left the market, triggering mayhem. IDR dropped to 80%. But over the subsequent years, the largest quintile of transactions has grown to 96% plus of total traffic. I feel this will be an important measure this year. It has recently broken below the 99% level; provided it can hold up, then the Bitcoin network will continue to be supported by the big money, which means a network collapse is unlikely. But if IDR drops again, it is telling you the big money has left for pastures new. The Bitcoin network The value of Bitcoin has always been linked to the size of the network. In the early days, the number of transactions followed price, but this broke down a few years ago and trading drifted “off-chain”. However, the metric that has stood the test of time is the transaction value. Bitcoin’s job is to transfer value over the internet. It therefore stands to reason that the more value it transfers, the more valuable it will be. I developed a valuation methodology back in 2014 using this principle (Figure 7). I am delighted to say that it still works! Figure 7: Valuing Bitcoin
Source: https://terminal.bytetree.com Bitcoin price and fair value past year
RESEARCH Over the past year, Bitcoin has tended to trade at a premium valuation, albeit it modest. Yet in the spring of 2021, that shot up. It was another signal urging caution; the price collapsed in May, along with the level of network activity, mainly because China banned Bitcoin trading and mining operations (again). This has caused a shock to the network. There are other indicators that are useful, such as the BTC inventories held by the miners pending sale. Here, when they aren’t selling, it is telling you the market bid is soft. Ironically, when they are selling, and price rises, that another reaffirmation of healthy demand. Macro Other useful indicators come from macro. Bitcoin is a risk-on asset, and the evidence for that is clear. It died in 2014 and 2018, yet thrived in 2013, 2017 and recently. Why? It was a risk-on environment which also saw strong performance from tech stocks. To summarise, the macro factors that are important are a falling dollar, rising inflation and rising bond yields - not dissimilar to how we would look for the good times in equities. It also moves somewhat counter to gold; in that sense, gold loves a falling bond yield within an inflationary environment, a falling real interest rate. In contrast, Bitcoin likes the yield to be rising within an inflationary environment. In that sense, both gold and Bitcoin are inflation-sensitive assets, with one doing best during risk-off times, and the other thriving in risk-on. What a dream combination of assets in an inflationary environment! I constructed an index that combines Bitcoin and gold called BOLD (Figure 8). The methodology uses inverse volatility weights over a 360-day period with monthly rebalancing. The results have been remarkable.
Figure 8: Bitcoin + gold = BOLD
Source: Bloomberg - ByteTree BOLD Index with &P 500, emerging markets and gold since 2014
I know Bitcoin did well in recent years, but this combination - which typically weights Bitcoin at 20%, with the rest in gold - has seen remarkably low drawdowns. And let’s face it: most of the past few years have been disinflationary. Imagine what happens when the money printing really gets going. I’m on record for gold $7,000 by 2030 and I still believe it. All we need to see is inflation expectations touch 4% while rates remain low. This scenario is entirely plausible, especially since there is nothing to keep a lid on the oil price these days. My post 2013 Bitcoin journey has been fascinating. I have assembled a great team of coders and analysts (and a fund manager). By my side is my co-founder, Mark Griffiths (son of the legendary Robin Griffiths), who heads the tech. He thrives on the elegance of blockchains. For me, it’s the challenge of analysing a new asset class that is here to stay.
Interdisciplinary Price Analysis Introduction by Patricia Elbaz, MSTA
Tom Bundgaard, MSTA MFTA Tom Bundgaard MSTA MFTA is Chief Analyst, Kairos Commodities and coauthor of ‘The Anatomy of Financial Markets Analysis’. He specialises in commodities from an analytical forecasting standpoint based on technical and fundamental analysis.
From Chocolates to Charts!
The constant companion
I knew I was onto a good thing when I put a through Zoom call to Tom, based in Copenhagen, who claimed that “good chocolate is one of my hobbies”! In fact, a New England Journal of Medicine paper published in 2012 came up with some interesting findings. Tom was delighted to point out that the research by Franz Messerli showed that there was a powerful correlation between chocolate intake per capita and the number of Nobel Laureates in various countries.
On your desk, among your two flat screen monitors, you likely have an invisible companion. It is nestling there as doubt, not being totally certain. It is a constant companion, and your technical analysis is there to remove as much of this uncertainty as possible. Today we will explore whether there is anything extra you can do to increase your odds and reduce your uncertainty - and with minimal extra effort.
So I can only thank Tom for pointing that out - I should by now be the winner or runner up of that prestigious prize! From chocolates we move on to charts as Tom’s research focuses on Inter-disciplinary price analysis. Tom recently gave a presentation to the STA on Interdisciplinary Price Analysis, or IPA. Below gives further detail on his research into this topic. Figure 1: The elements of IPA
“Here you are not using just technical analysis, but also fundamental analysis and macro analysis. The aim is to gain multiple confirmations and evidence stacking which will make your conclusion more certain and more broadly fact based.” Tom Bundgaard, MSTA MFTA
Figure 1 shows what I have termed ‘Interdisciplinary Price Analysis’. Here you are not using just technical analysis, but also fundamental analysis and macro analysis. The aim is to gain multiple confirmations and evidence stacking which will make your conclusion more certain and more broadly fact based. At this point you will seriously question my point about “minimal extra effort”, but I will put your worries at ease in a short while.
RESEARCH Figure 1 not only shows the pyramid, but also the three dimensions of X, Y and Z. Today we will focus on the Z-axis, marked by a circle. The X- and Y-axes will be left for another time (or you can see the full model in the recent STA webinar from April 2021 in the archive). Let us get straight into the thick of it, using a random commodity price as an example to showcase the analysis. Double and triple confirmations If technical analysis is your central tool kit, then you are probably using the price (in this case, sugar prices) together with moving averages, RSI, MACD and other tools. This is shown in Figure 2 with only a moving average as a very simple version. In this graph you can also find the world inventory for sugar, which is a fundamental data series. Not only are we using the fundamental data, but we are also applying a similar moving average to the inventory level to define the start of uptrends and downtrends. Figure 2: Example using sugar price analysis
As you may have deduced already, the main point is that inventories breaking below the moving average is a confirmation of the buy signal you get where sugar prices break above the moving average. That is a double confirmation where you get the luxury of working with much more certainty because two different types of analysis (that do not ‘know’ about each other) are confirming one another. This is both very simple and very effective - and that is the benefit of using interdisciplinary tools. In Figure 2 there are three grey arrows that show examples of this kind of double confirmation, but a few practical pointers are needed: a) Lead-lag: The grey arrows shows that the break above/below the moving average (for price and inventory) does not occur precisely in the same month. That would naturally be too much to ask for. Inventory can sometimes be a leading indicator for price and sometimes a lagging indicator, and you need to study this. This lead-lag means that you should normally expect a short delay before you get the double-confirmation. However, this waiting is very short compared to the long trends you enjoy afterwards where the signals are aligned.
RESEARCH b) Full technical analysis: It would not really be satisfying for you as a technical analyst to restrain yourself only to the moving average for the inventory data. Let us add RSI and MACD, which is shown in Figure 3. Here, the blue arrows indicate where a low RSI forecasted an imminent low in inventory levels (and this is working equally well with a high RSI that indicate peaks in inventory). The RSI works exactly the same way as when analysing a price. The whole point of this exercise can be shown where we mark “May 2017” as an example where inventories broke above the moving average. The problem was that prices had already broken below their moving average two months before (seen in Figure 2). As described in the “lead-lag” above, this leaves you with two months of uncertainty before you get the double confirmation in May 2017. In reality, of course, there was very little uncertainty. Figure 3 shows an extremely low RSI for sugar inventories already at the end of 2016, and as a technical analyst we would expect a turnaround on the horizon and an increase in inventories. That is the advantage of the RSI - it means that we may not need to wait the two months for confirmation, all the while knowing that it is likely to come. As you can see, this approach is all about evidence stacking and understanding the whole context. You can take any point in Figure 2 and Figure 3 and construct a similar convincing story because it consistently makes sense. Figure 3: “Stealing” the vital data
We are actually breaking a certain “interdisciplinary barrier” in Figure 3; not only are we stealing data from the fundamental analysts, but we are also applying technical analysis on this data. What intrigues me is that we can wring so much more meaning out of this inventory data than the fundamental analyst is able to - simply because we apply our technical tools on their data. This can be done on any fundamental data series such as cost driver, speculation data, supply-demand data etc. I see this as the technical analyst’s edge. A fundamental analyst would have a hard time believing that inventories would stop falling in spring 2017 and start a new increase, simply because “some mathematical RSI” or “some moving average” was showing it as highly probable. We do not have the same constraints. Triple confirmation: macro Triple confirmation is naturally better than double. To make this the case, we need to deploy the macro economy, as we know this has an impact on stocks, bonds, currencies and commodities - which is why any analysis without macro is incomplete.
RESEARCH You simply construct the same graph as in Figures 2 and 3 but with macro data, which could be GDP growth or the Purchasing Managers Index (which is a great early indicator for GDP growth). Without using yet another graph, clearly the growing macro economy since spring 2020 has put upward pressure on commodities in general, including sugar. Hence the increasing sugar prices from 2020 to 2021 give you your triple confirmation. Just as simple, just as effective, but now with three confirmations. This should boost your confidence infinitely more than if you relied on technical analysis alone. The way I see it, the three types of analysis give me their conclusions in three different “languages” (like English, Chinese and German). If you understand the language (which you do because you can interpret the technical tools) then you can see that they are in agreement with one another - which can be called evidence stacking or Interdisciplinary Price Analysis.
Pareto The only thing missing now is to calm your concerns about the extra work. You likely have your plate full already with your own analysis and do not want more work, thank you very much. I agree. That is why I do not suggest conducting a full fundamental and macro analysis on top of your technical analysis. Instead, I suggest using the Pareto principle and find the 15% data that makes up 80% of the value. In the case of sugar, you need only the world inventory of sugar to get a very solid indicator and understanding of the market. In practice you go to the USDA database, download the inventory data, and then apply the technical tools. When you have made the first setup it will only take you a few minutes in the future to update this monthly. Very little work, using just two minutes, gives massive benefits. Granted, you will not get 100% of the value of a full fundamental analysis, but you do not want to get bogged down in analysing cost drivers, speculators, supply and demand and 10 other factors. Just find the one indicator that has a massive importance. Inventories and macro data are updated monthly, so in this case we are talking mere minutes of work per month. A small price to pay for knocking Mr Doubt off your table. We have been talking about sugar in this example, but you are likely to be interested in stocks or bonds or currencies, and the same applies here. It is a matter of reading published fundamental analysis of your preferred stock and find out what data series they are pointing to as important. Then you simply track this using the IPA approach. When I started out in technical analysis, I was told that there was (or had been) a war between TA and fundamental analysis. Fortunately, I have not really seen a trench war between these two camps. Instead of war, I am seeing both sides being very, very busy in their own little trenches to bother taking a stroll to the other side to get some inspiration, tool or data. I here make the case to take such a stroll. You may just stumble upon something valuable.
Exploration of a trading strategy system based on meta-labelling and hybrid modelling using the SigTech Platform In June 2021, Petri Nousiainen submitted a thesis on the exploration a of trading strategy system based on meta-labelling and hybrid modelling using the SigTech Platform for his Masters of Engineering in Big Data Analytics. The aim of the thesis was to find a machine learning-supported trading system. The trading system was coded with the Python programming language in the SigTech platform, utilising SigTech data.
Petri Nousiainen, CEFA Petri Nousiainen, CEFA is Director of Investor Relations Europe at FireFox Gold Corp, Finland and has a professional background in trading equity, equity index futures and energy futures for over 15 years.
“His study incorporates the financial theory of information efficiency and the efficient market hypothesis. It explores technical analysis, trend following, trading strategies, futures contracts and fixed fraction asset allocation.”
His hypothesises was that machine learning (ML) has the potential to improve any trading strategy by addressing the binary classification problems of predicting profitable trades with probability and with sizing the investment. His study incorporates the financial theory of information efficiency and the efficient market hypothesis. It explores technical analysis, trend following, trading strategies, futures contracts and fixed fraction asset allocation - including bet sizing by the Kelly criterion. Nousiainen explains how to predict the conditional probability of the next trade’s profit. He also looks at another ML-supported process that uses inclusion of binary prediction utilising meta-labelling for combining predictions as a hybrid of a primary and secondary models. Finally, he also discusses the logic gate for validation. The study methodology explains the setting of the trading system hierarchy, including theory, system, strategy, process and signals. After presenting the SigTech quantitative research platform, it explains the descriptive statistics of the data and the features construction and how the Tail Reaper and MNIST models contribute to designing the final version of the coded trading system. The trading system structure consists of the primary model (Donchian channel), the labelling (binary classification) and the secondary ML model prediction of profitable next trades using probabilities to size the bet. The logic gate rule validates the making of trades according to the designed trading system when both parts of the hybrid model produce valid results (see Figure 1). The CAGR makes it possible to compare the performance of trading strategy returns increasing or decreasing over time, and the Sharpe ratio measures the risk adjustment. Finally, the study describes the technical analysis indicators used as features, the Donchian channel trading strategy, meta-labelling and the logic gate method for validation of the hybrid model. The algorithmic description presents the random forest classifier that produces multiple decision trees, leading finally to the simple output of a binary prediction with the probability prediction. The feature selection is a way of improving the transparency and the interpretability of the random forest ML algorithm. The feature importance table shows the ranking of the features and enables feature analysis and selection to be refined.
RESEARCH Figure 1: Constructing the trading strategy system
System Construction • The designed trading strategy system structure
The Trading Strategy System Structure Face
Features (48): 17 rolling front futures + 1-day historical returns + 14 technical analysis indicators
Primary / Base Model (technical analysis model): INPUT: S&P500 E-mini front future (target feature, 5557 close days, 2000-01-04 to 2021-04-21) MODEL: Donchian Channel (channel 50, exit, 10) OUTPUT: daily percent returns (positive and negative)
Labeling / Binary Classification [0,1]: Binary classification of Donchian Channel output (1 = profit, 0 = else) (Metalabels [0,1])
Secondary Model (machine learning prediction): INPUT: features + labeled output of the primary model (= target variable: (Metalabels [0,1]) MODEL: Random Forest OUTPUT: Prediction (profit = 1 or not = 0) and probability e.g. (0.35, 0.65) of tomorrow's close price
Rule = > If both Primary and Secondary model perform true (1): Then positive (do the trade), else negative (no trade)
Labeling/ Binary classification [0,1]
Features + Primary model output
[0,1] Primary model correct [0,1]
If both are 1 then positive, else negative
The primary model’s outputs are daily percentage returns that are converted to one if positive and otherwise to zero. The output of the primary model is compared with that of the secondary model prediction. The trade is carried out if both parts of the model give an output of one. The feature importance table generates the highest ranking for the RSI momentum indicator and the lowest for the two-year US Treasury note. The maximum probability prediction is 0.73 and the minimum is 0.27 for the test period of 917 days. Figure 2: The results of testing the system
Empirical Results • The Cumulative Returns of the Underlying, Primary and Secondary The value of the probability prediction
The value of the machine learning algorithm
Buy and hold strategy of the underlying (Benchmark)
Nousiainen’s results show that the secondary model’s random forest gives an F1 score of 0.648, an AUC of 0.560, and a test accuracy of 0.555 compared with the related research of cMDA giving 0.576 (F1), 0.779 (AUC) and an accuracy of 0.583. The cumulative return of the trading strategies gives an underlying profit of 0.61, the primary model 0.38, the secondary model (profit or not) of 2.80, and the secondary model 3.15. The validation of the results of performing the historical trades (according to the trading signals) with an initial capital outlay of 100,000 monetary units generates 61,070, 38,700, and 280,600 of profit
RESEARCH on the underlying, primary and secondary model trading strategy, respectively. The machine learning of the secondary model shows significant value-added compared with the underlying and the primary model, generating about five (517%) to eight (816%) times more profit during the 917-day test period. Figure 3: Validating the results
Empirical Results • Validation of Results • The initial capital of 100 000 units invested, 917 days
Validation of Results (917 days) Trading Strategy
Value to Initial Capital
Value to Primary Model
Value to Underlying
Value to Probability Prediction
1. Initial Capital (100,000)
2. Underlying (Buy and hold)
3. Primary Model (Donchian channel)
4. Secondary Model (Profit or Not)
5. Seconday Model (probability)
6. Optimal Bet Size (Kelly criterion)
These results indicate that the hypothesis is valid, namely that a machine learning algorithm can improve a trading strategy. In this study, the machine learning model predicts a binary classification (profit or not) and a probability of the next trade. Consequently, integrating a machine learning model with a trading strategy of a similar system structure, as in this study, can add value and consequently improve the strategy. To summarise, based on the implemented trading strategy system, results indicate that by exploring the ways of constructing a hybrid model it is possible to achieve an explainable solution with potential for scalability, repeatability and interpretability. The results show that the solution achieves improves accuracy, enabling interpretation and - importantly - constructs a repeatable process. A key finding is that the process is scalable in the following ways: • • •
First, we can choose any trading strategy or technical analysis model as the primary model, label the output accordingly and then further improve with a machine learning algorithm. Second, we can choose any liquid financial instrument as input data instead of the futures contracts. Third, the data period is scalable from daily to monthly and yearly frequency. The period may even be hours or minutes.
All these parameters can be changed and tested without impacting the implemented design of the trading strategy system based on meta-labelling and hybrid modelling using the SigTech Platform. The most significant finding is that labelling is the essential tool that allows the machine learning algorithm to improve other trading strategies. A valuable finding of study is that the secondary model, the machine learning model, also predicts when not to open a trading position. "Knowing when not to bet is as important as knowing what bets are probably worth making." - Ray Dalio To read the full text of Petri’s thesis click here Our thanks to Petri for sharing his Masters thesis research ’Big Data Analytics’ with the STA.
A Three-Point System to Find HighProbability Assets Introduction Picking high-probability assets for the portfolio forms the foundation of a portfolio that performs consistently well. There is, of course, the next stage: extracting profit safely for the duration the asset is in trend and where a strategy comes into play. However, if you regularly pick dud assets, your portfolio will always struggle to gain traction no matter how good your system is. So how do you pick good assets? There are two techniques: fundamental analysis and technical analysis.
Fundamental Analysis (FA)
Zaheer Anwari is the co-founder of sublimetrading.io, mentoring busy professionals and business owners to build investment portfolios around their busy lifestyles.
It is arguably more suited for institutions that pay significant sums of money to get news items early. For private investors, this is an approach that does work but can be confusing and time-consuming. Everyone wants to be the next Warren Buffett. Still, if you look at performance tables of the best traders and investors over the decades, Warren Buffett regularly comes midtable at best. Trend followers such as Richard Dennis will feature at the top end, suggesting that technical analysis is a more suitable and a more profitable approach for everyday people.
He is also the co-founder of The Gryphon Fund, for savvy time-poor investors who want to maximise the potential of the financial markets without the time commitment needed to personally create and manage a portfolio.
Technical Analysis (TA) This is where we look at charts to make our investment decisions. TA is the savvy way for professionals and business owners to create a portfolio around a busy lifestyle without compromising on time and profit due to its simplicity of execution. The key to success is being smart enough to use the higher timeframes, namely the monthly, weekly and daily. This article will focus on using the monthly timeframe and the three-point system I use to pick top high-probability stocks, commodities, and currencies. After all, a chart is a chart. If I was to show you four charts, a UK stock, a US stock, a commodity and a currency pair, your job as an investor is to pick the asset that is most likely to return a profit. You base this on what the chart is saying, not on any preconceived ideas or opinions. As a reminder, one of the first rules of investing is to ignore opinions, including your own. A Sidenote The term 'trader' or 'trading' has been tarnished since the creation of the internet, allowing quack educators and scam schemes to flourish with little to no accountability. These terms are associated with people sitting in front of their computers or hooked to their mobile phones all day and generally losing money. This is the case if you choose to use the intraday timeframes (hourly, minutes, seconds) and where the odds are stacked against you to achieve any success. It is our job to educate the masses and bring a more positive spin on these words. A trader is someone who chooses to use charts to make their decision. The type of trader that you are is determined by the timeframes you use. The best approach
RESEARCH for busy everyday people is the combination of using charts as a trader but holding positions for the medium to long term as an investor. This allows people to do less by taking fewer positions but make more profit through holding and compounding. The Monthly Timeframe -Point System Technical analysis and, in turn, good investing, is simply a process of pattern recognition. If an asset has performed well in the past, it drastically increases its chances of performing well in the future. The patterns we look for are called long-term trends. These are sustained moves to the upside, a bull trend, or the downside, a bear trend that can last weeks, months and years. This is where the monthly timeframe comes into its own as it answers the following three questions:
• What did price do in the past? • What is price doing right now? • What is price likely to do in the future?
These questions form the monthly three-point system. I am going to use the monthly timeframe of Ashtead Group (AHT) equity prices to expand on each question. What did price do in the past? AHT, a UK stock, has a history of trending well. I have highlighted two periods of trend that lasted circa three years each (Figure 1). This tells us that AHT is very likely to perform well and hand out good profit going forward. This requires you to become good at eyeballing charts and identifying good charts from messy charts. Figure 1: AHT is very likely to perform well
What is price doing right now? AHI have taken the chart back to October 2020 (Figure 2), as this was when AHT was first picked up by our scanners last year. In addition, I have highlighted the circa two-year period of consolidation price was in, swinging between £10 as support due to CV19 and £30 as resistance. Consolidations act as excellent foundations for trends to develop. You may have heard of the
RESEARCH expression the longer the consolidation, the bigger the breakout. This holds particularly true on the monthly timeframe. I like to look for periods of consolidation of at least three to five months. Experience has taught me that those trends that unfold once price breaks out are significant. Figure 2: Consolidations act as excellent foundations for trends to develop
What is price likely to do in the future? This is the million-pound question that everyone wants a crystal ball to answer. The answer, of course, is that there are no certainties, just probabilities. However, by placing close attention to points 1 and 2 above, you drastically increase the chances of profiting from emerging long-term trends. Of course, not every position will work out, but that is where risk management and stop losses come into play. If one thinks in terms of profit (the investor mindset) and not winners vs losers (the gambler mindset) the portfolio will perform far better over the long term. The mantras of the “trend is your friend until the bend at the end” and “cut your losers short, let your winners run” always hold true. When it comes to how an asset could perform going forward, I keep it simple and use round numbers as levels that the price is likely to move towards once in a trend. I then follow with a trailing stoploss. This removes guesswork, opinions and predictions, which are the downfall of many, and I let price dictate.
RESEARCH Figure 3 shows the monthly timeframe post-October 2020. It shows how the trend has moved to £40, £50 and is now finding resistance at £60. A break and close above £60 will see price move towards £70. Price has moved almost 100% since October 2020. Figure 3: Remove guesswork, opinions and predictions
Other stocks in my portfolio that I have applied this logic to, following the recovery in the market from Covid19, are CDNS, BRK.B, DE, CUBE, EXR, IDXX, NVDA (all US) and CRDA and PCT (both UK), amongst others. I have also been short on the USDCAD since December last year. Using Technology To speed up the process, I subscribe to scanners that I have programmed to create an initial shortlist. I conduct scans daily based on the closing prices of the day. Next, I then use the bespoke charting and execution tools I have developed to swiftly apply the manual the-step analysis process described above. This combination of scanning and manual analysis allows me to build a balanced portfolio across the best-performing stocks from the best-performing sectors. Throw in commodities and currencies, to follow the flow of money out of stocks, and you have a system in place which will never have you short of highprobability assets. A chart that the scanner has recently picked up and that has made it into our portfolio is Brookfield Asset Management (BAM). I will leave you to see if you can apply the three-step process above.
Elliott Taken On - Shape and Price Introduction: a hard sell Was Elliott right? Presenting his ideas for the first time to rational people, you’ll meet a wall of scepticism. The idea that prices move up and down in some sort of rhythm made up of waves seems far-fetched. Investors’ heads are already filled with their own ideas, which probably include the conviction that share price moves aren’t predictable. So Elliott is a hard sell without hard evidence. Fibonacci Ratios in our charting programmes are a hard sell too. Can you answer in a sentence ‘Why do Fibonacci ratios work’? And what do you mean by ‘work’? Getting people to see the relevance of Elliott and Fibonacci to price forecasting needs rational explanation of the concepts involved, rather than trying to explain ‘the secret of the universe’.
Christopher Mack, MA(Cantab), MBA, MSTA Christopher Mack studied Engineering (all varieties) in 1961, followed by Business School. After careers in industry and management consultancy he became a private investor. That started a quest to ‘beat the market’ using technical analysis. Twenty years later he concentrates on market timing, while trading UK shares with trends of several months. He also coaches others and seeks to de-mystify Elliott’s ideas and make them clearer and more useful to all.
Elliott Wave Theory. Image by Julie Bang © Investopedia 2020
Elliott’s key legacy was to draw out a set of shapes - waves - each built of either five or three subwaves. He showed that these are connected in stock markets, and that all charts are made up of different sequences of them. He believed that the groups of five and three subwaves build into larger waves - and subdivide into smaller shapes - which look the same as the original shapes. He had in effect discovered financial fractals by defining the shapes he categorised and showing how they scale up and down in degrees. But Elliott left only simple diagrams, with no measurements to show their proportions. Twenty years later, in the 1960s, a Yale University Professor of Mathematical Sciences, Benoit Mandelbrot, invented the mathematics of ‘fractals’ - the idea of shapes built entirely of similar smaller shapes. His fractal is ‘a pattern or shape whose parts echo the whole’. He applied his broad ideas to financial markets, and showed how to build models of price charts which included all the apparent randomness of real-life moves yet with underlying mathematical form. His interesting book ‘The (Mis)Behaviour of Markets’ is recommended to everyone - a maths book with no equations until some notes at the end!
RESEARCH Mandelbrot built up price models mathematically from simple shapes. In the simplest, he took a straight ‘Line’ representing a price move on a chart as his starting point, then a ‘Generator’ which was a zigzag of known proportions. This was the building block of his model; wherever he saw a Line (a single wave, in Elliott’s terms), he replaced it with a Generator, the 3-wave zigzag. The generator’s own 3 waves were smaller Lines - replace each of them with a smaller generator, stretched or shrunk to size, and the fractal shape evolved. Add in another rule - he could scramble the order of the generator’s 3 lines, and his evolving form looked ever more realistic as a price chart. To show the link between Elliott and Mandelbrot, Figure 1 shows a set of shapes and a stock market ‘cycle’ of a bull and a bear wave (Elliott), and lines and generators (Mandelbrot). In simple terms, we can show this five-wave then threewave form (Elliott’s ‘cycle’) as two connected generators 0-1 and 1-2, which repeat to grow into another cycle at the next higher Degree 0-1 and 1-2 (see Figure 1): Figure 1: A stock market ‘cycle’ incorporating Elliott and Mandelbrot elements
For our own market model, we want to find the likely sizes of Elliott’s shapes: put those together with their likely sequence, and we have a forecasting tool. TA has tenets which help us forecast prices - Support and Resistance, and a hidden one we don’t often notice. More questions: why does charting software have the ability to flip a chart upside down about a horizontal line, inverting price? And shouldn’t it also be able to mirror the chart from right to left about a vertical line, reversing time? What is it we see when we flip or mirror? I think the hidden answer is ‘Symmetry’. We think we see that wave patterns, fives and threes, look just the same - and repeat - both upside down, and forwards and backwards. If this is true, we have a foundation for building a price forecasting model using symmetry and shapes. If that ‘works’ by producing a high probability forecast to an acceptable accuracy, we must be on the right lines. Here is the proposed foundation for a possible model, which I call the ‘Bouncing Price Conjecture’ (BPC).
The Bouncing Price Conjecture • Price ‘bounces’ up and down between ‘levels’, where it reverses direction. Each of these can become a support and resistance level. •
Price BEHAVES AS IF it is a rubber ball (let’s say it’s a red one), which bounces back up a fraction ‘f’ of the height it is dropped, at every bounce. So on the second bounce the red ball (= price) comes up to fraction f x f of its original drop, and so on at each bounce. The red ball is tracing out a falling series of possible Resistance levels, at f, f², f³, f⁴ and so on above the ‘floor’.
Now the BPC adds Symmetry. For this action to be symmetrical we have to imagine a similar green ball which bounces off the ceiling, not the floor, so every bounce rises nearer the ceiling. The two balls have the same elasticity, so f is the same for each. The green ball is tracing out a rising series of possible Support levels.
If f < 0.5, the balls won’t meet anywhere; but if f > 0.5 then we could suppose that the level of green’s first bounce coincides with red’s second bounce, or vice-versa. Then what would f be?
Answer: red’s second resistance is f x f off the floor, green's first support is 1-f off the floor; support becomes resistance when they meet and these are equal. So we get the quadratic equation: f² + f =1 and one of its solutions is f=0.618, also known as the Golden Ratio (GR).
At what points does Support become Resistance, and vice-versa? One case is when f is 0.5, so the two price levels meet exactly half-way on the first bounce.
Figure 2 illustrates the BPC conjecture: Figure 2: The red and green balls illustrate the Bouncing Price Conjecture
If we accept the BPC as a logical foundation, we come straightforwardly to the usual ‘Fibonacci ratios’ as potential supports/resistances at a series of Levels. They are a simple consequence of Symmetry in charts, mathematically situated at Powers of the GR (reds), and their Complements (greens) - I term these both ‘golden proportions’.
RESEARCH The Standard Forms The next question is how do Elliott’s patterns fit these Levels? You’ll have spotted in the diagram a descending and an ascending triangle; these fit his finding that alternate waves often are related size-wise by the GR. If we can also find where all his other patterns would sit in these levels, we will have a set of ‘standard forms’ we could use for forecasting price. First, we need a convention for describing Levels. Label the start of each 3- or 5-wave as point 0, and its corresponding Level on the chart as L0. The other levels correspond to the points at the end of each subwave, so a 3-wave correction has levels L0,LA,LB,LC, and a 5-wave’s levels are L0 to L5. Give starting level L0 the value 0, and the final level (L5 or LC) the value 1.0, then fill all the intervening levels in at golden proportions. The result is a table of Levels. Table 1 shows the five (rising) standard forms. Table 1: Rising Impulses and Diagonals
1.0 0.5 0.382 0.236 0.090 0
1.382 1.0 0.764 0.382 0.236 0
1.0 0.910 0.5 0.618 0.146 0
1.0 0.910 0.382 0.5 0.090 0
L5 L3 L4 L1 L2 L0
These forms have symmetry - the extended 5th Impulse is the flipped version of the extended 1st; likewise the expanding diagonal is the mirrored-and-flipped version of the contracting diagonal. Why choose these various figures? I look for shapes that commonly recur in charts stock indices, my area of interest. In particular, real-world chart shapes often have proportions with at least one level at 0.618 or 0.382, so I’ve sought to include one of these in each shape. The other figures also fit into profiles often seen in each shape. For example, in a contracting diagonal you can find that wave 2 retraces wave 1 deeply by at least the proportion 0.618 - so the standard form above does so by 0.764. (Wave 1 is 0.618 long between L0 and L1; retrace 0.764 of that and you come down to L2 at 0.146). The Extended 1st and 5th impulses have levels defined primarily by their 0.618 and 0.382 levels, as do the two diagonals. Those two levels define the standard form, and the other levels slot in around them to flesh it out. But the Extended 3rd Impulse has a quirk - this is to accommodate its tendency to include an ‘alternate’ proportion internally. It often has its wave 3 as a 1.618 extension of wave 1, then wave 5 develops as an ‘alternate’ to waves 0-3, at 0.618 times their length. So points 0 and 3 define L0 as 0 and L3 as 1.0, but point 5 goes above, and L5 is often seen at 1.382. Here are some standard forms for Elliott’s Corrections: Zigzags: Here, I’ve extended Frost & Prechter’s notation of W,X,Y,X,Z waves by labelling levels X1 and X2 as the first and second X’s in the triple zigzag. The double and triple zigzags are just forms of ‘extension’ in Elliott’s terms; and I see the single and double as ‘threes’ and the triple as a ‘five’, a corrective form it shares with the triangles. Table 2: Falling Corrections
Zigzag single L0 LB LA LW
Zigzag double 1.0 0.618 0.382 0
LW LX2 LY LZ
0.618 0.5 0.382 0
RESEARCH Flats: These are easy, but we have to allow for the Expanded Flat jumping out of the ‘box’ of the Regular Flat to point B, so LB > 1.0. Likewise it jumps ‘below the box’ to point C, resulting in a negative value for Level C; this preserves the often-found geometry of the shape. Table 2: Falling Corrections
Expanded Flat 1.0 1.0 0 0
LB L0 LA LC
LB L0 LA LC
Running Flat 1.382 1.0 0 -0.618
LB L0 LC LA
1.382 1.0 0.382 0
Triangles: The two symmetric triangles include the odd-looking levels of 0.472 and 0.528, necessary to preserve Elliott’s 0.618 proportions between waves, and symmetry of the shapes, which can be easily flipped and mirrored. The Golden Proportions of those numbers are 2 x f³ and its complement. Table 4: Triangles
Symmetric Triangle L0 L2 L4 L5
1.0 0.618 0.528 0.472 0.382 0
Reverse Symmetric Triangle L5 L3 L1 L0 L2 L4
1.0 0.618 0.528 0.472 0.382 0
Ascending Triangle L1 L3 L5 L4 L2 L0
1.0 1.0 1.0 0.618 0.382 0
Descending Triangle L0 L2 L4 L1 L3 L5
1.0 1.0 1.0 0.618 0.382 0
This method is powerful, because it can also cater for Elliott ‘Anomalies’ like Slanting Flats, Running Triangles with short first waves and Three-wave Triangles (3-3-3’s). These are all valid shapes under this system. And another anomaly - the Truncated fifth - can be seen as an overshot third, so its point 5 does end at L5=1.0, with L3 being >1. That releases the idea that its symmetrical anomaly is an overshot 2nd wave, terminating below Point 0! We do see those. You can of course substitute your own figures in the tables, if you think they ‘work’ better, to produce shapes more often found in real-life charts. You may also want a small range of possible standard forms for each shape. For example, sometimes you find an Extended 3rd which has symmetry with wave 1 = wave 5, or with wave 3 vertically central in the shape but with non-GP levels. The levels don’t all have to contain 0.62 or 0.38 values. (There isn’t room in this article to describe them all or show all the shapes.) So far we have Support and Resistance giving us Standard Forms of the various Elliott shapes as Levels. These are easily turned into Shapes graphically - Figure 3 shows three more complex ones:
RESEARCH Figure 3: Some more complex Elliot shapes
Figure 3 shows how the shapes would look on a log- or linear-scale chart. I work solely with log-scaling as linear scaling is impractical for long-timescale charts of share prices. These proportions can then be used to calculate the ‘standard’ value of each point at its corresponding level - more easily done on a spreadsheet. Primary Perfection The next idea is to define ‘Perfection’ in a shape, a concept a statistician can test against if we can provide them with examples within an acceptable tolerance. Suppose in an Impulse our first wave rises from L0 = 0 to L1 = 0.382, and our fifth wave rises to L5 = 1.0. This is an example of ‘Primary Perfection’ in a shape - that any two points can define the third when you know their levels, and when all the levels are golden proportions. (‘Secondary Perfection’ develops this idea, where any three known points and levels can define a fourth, but it is not used here). The Standard Forms above are all Primary Perfect. They give us something to compare the Actuals with (within tolerance) and be able to test for ‘Hits’. If we can show plenty of examples of Hits, that will support the Bouncing Price Conjecture. Figure 4 shows spreadsheet example of how to do so. Figure 4: Seeking perfection
RESEARCH Its aim is to take a developing Impulse or Diagonal where we know the price of points 0-4 (from 100 to 240 entered here), and we want to find where point 5 should be to generate perfect levels. We take an estimate for point 5’s price (=750 here) and the spreadsheet calculates what the primary-perfect price of point 5 is from each of points 1 to 4 at each of the different levels. The Hit test sensitivity is then used to filter out display of all out-of-range calculations. I think a Tolerance at a maximum of 3% is acceptable (as here), but less is better and improves the quality of the perfect Hit. In the example the best estimate is obviously 757 for point 5, which will produce two near-perfect hits for points 2 and 3. That would make it a high-probability forecast, but not the only one, as this trial-and-error sheet can find other targets that also have two Hits. It’s not much work to search them out: in a rising market if the lowest ‘perfect’ target you find does not become a significant turning point, look out for the next higher one. Now we enter the real world. Two points are strong Hits, but the other two aren’t - the 0.455 and 0.434 levels aren’t close to GPs. We may satisfy the statistician about the BPC, but the Shape isn’t a Standard Form - it’s at best a Contracting Diagonal with an overshot fifth. But its Perfection comes from Hits on Perfect Levels, not from perfect internal proportions. And the same applies to the Standard Forms themselves - some but not all of them will have waves where the proportions are ‘perfect’, like wave 3 = 1.618 times wave 1, which is one conventional way of thinking about Elliott shapes. Now we can answer the questions we started with: • Was Elliott right? In terms of discovering a set of fractal shapes validated by Mandelbrot’s maths, yes. Is the set complete? Well, I do think it needs the Three-wave, 3-3-3 Triangle including. I would also challenge the rigidity of Leading Diagonals always counting 5-3-5-3-5, and Ending Diagonals counting 3-3-3-3-3. I do think that these counts should be no more than a Guideline, as mixed fives and threes do occur in the subwaves. • Do Fibonacci ratios work? A modern TA concept, they are derived direct from the Golden Ratio, which itself can be derived three ways: Arithmetically from adding any two numbers, Geometrically from dividing a line in a certain proportion, and Algebraically from a general equation. The BPC derives the Golden Ratio algebraically to design a set of Levels. The ‘perfect’ levels can be used to test the turning-points of real-life waves against, to an acceptable tolerance, and see whether the number of Hits has statistical significance - one for a budding PhD student? Summary and Acknowledgements Most technical analysts will have met Elliott’s work through Frost and Prechter’s ‘Elliott Wave Principle’. Peter Goodburn of Wavetrack International is also highly recommended for his ideas on the development of form and proportion of waves, including anomalous waves. Two new ideas have been developed here. Levels derived from the BPC use the Golden Ratio to define perfect versions - Standard Forms - of the shapes Elliott conceived. Primary Perfection gives us a tool to forecast at what price a wave might optimally end, even when the wave is partly imperfect, as in real life. I do hope these ideas have helped you think afresh about Elliott. Comments would be welcome to email@example.com. Elliott Wave Principle, Frost and Prechter.
Head and Shoulders above: Steve O’Hare, MSTA How I lost my hair! A look at the standout moments of Steve (Nohare) O’Hare’s career in trading - the shocks, the on-the-money predictions and how tech has changed the way technical analysts work. Steve is Co-founder and COO of trading insights service Signal Centre. Full luscious 80’s buffon I remember being in awe of my Dad and how he would calculate sums as quick as a PC (in the old days at least, when PCs were a bit slower). He must have passed it down in his genes to me as I realised I had a similar skill at school. I wasn’t gifted at all and I had to work hard to get the right results but I was able to pass my GCSE maths a year earlier than the majority of my peers. However, this led to a more than slightly nonchalant approach to my A Levels which I failed miserably a year later.
“...in 1998 God invented computers! And the world came tumbling down. Well, that’s what it felt like. Thousands were literally dragged kicking and screaming to the desks. It was a sad time; the open outcry world was gone.” Steve O'Hare, MSTA
In part, this may have been because the bright lights of the city were far more attractive than A levels and within a few months I was signing up to financial and computer focused employment agencies. But my initial excitement waned when I had to end my first role as a computer operator after only a year as the shift work interfered with my football, where I was earning just as much for turning out on a Saturday as I was during the week! Short back and sides My taste for the city was still present though and I was invited for an interview at the Grain and Feed Trade Association (GAFTA) where I was offered a role as a
ANALYST FOCUS board marker on the Wheat Futures. My preparation for the interview was disastrous! A visit to Sweeney Todd's barber the day before resulted in what can only be described as a nuclear disaster, with my bushy barnet looking more like a mushroom top. I even stopped on the way to the interview the next day to ask another barber to fix it. However, the feedback from Bill at GAFTA (ex-army & wannabe pro- footballer) was that he was super impressed by my passion for football, Prince of Wales check suit and tidy barnet and I got hired. Board marking sounds boring but I loved it. Watching the traders buy and sell was my first experience of anything like this, apart from watching Trading Places back in 1983! My job was simple - every time a price traded, I needed to mark it on a white-board so traders could reference it later. Even in the early days, I started to see small patterns and recurring levels but had no idea these were the ingredients to charts. One day a guy showed me a point and figure chart, I remember thinking he was nuts but he seemed to do OK trading for himself. It wasn’t long before one of the brokers needed a junior and I was pretty well placed with a good understanding of Soft commodities by then. The long three-hour lunches were greatly enjoyed by the senior traders in the Baltic exchange bar. However, I found a more interesting vice - assisting on London International Financial Futures and Options Exchange (LIFFE). This experience turned out to be a saving grace as business and opportunities dried up on the Baltic. Hairs standing on edge WOW!!!! IT WAS AMAZING!! This was where I wanted to be and this will always rate as a major highlight in my employment life. In brutal honesty, to say I did not even understand the actual products that I traded on behalf of the firm or myself back then, is a big understatement - but it didn’t matter. There I’ve mentioned the F word - Fundamentals. I’ve even just said ‘They didn’t matter’. This might upset some people but it’s nothing personal. There is room in this world for all types of analysis and combining different forms is important (more about this later!) but two decades ago it didn’t matter... In the early days I would spend time peering over the shoulders of the studious traders and watch them connect the dots and manually draw their line charts. The noughts and crosses had been replaced by more straightforward lines and bars. I would also pick up a morning report with technical levels highlighted and I would use these levels and benefitted from them on many occasions. I then started to draw my own very basic charts and watch the price action around the key support and resistance levels. At this time, the support and resistance levels were previous highs and lows and, in my opinion, this has still not changed much. Then traders would come to me for the levels and although it was a dog-eat-dog world, there was still time for some form of comradery. Pivot levels really worked back then and the more people who knew about these levels the more relevant they became. It was almost self-fulfilling! Mind you during Non-Farm Payrolls (NFP), for a brief period of time the only thing I would rely on is Noise Analysis, a now outdated and unsophisticated form of sentiment analysis. I became accustomed to understanding and reacting to different types of noise. You could tell the difference between a panicky trader trying to sell or a desperate trader trying to buy. Sounds weird - right!? Technicals were still important but had taken second place to noise. There was one time I was standing in the Bund pit and noticed a flash news headline “SNB cut interests rate 0.25”. I ran 30 feet into the Swiss pit - it was silent.. then I heard the anguished screams of a UBS order clerk trying to pull their offer from the pit trader. This type of noise was a clear give away and before the trader could react, I took his 200 lots off his hands. At the time I didn't know whether the rate cut was expected but had a good idea (based on the phone clerks panic) and the fact the price was instantly bid 10 ticks higher - I had made a great call. Risky but great... I have some fantastic stories of great and bad trades that I made over the years, but this is one of my favs! It all starts falling out Then in 1998 God invented computers! And the world came tumbling down. Well, that’s what it felt like. Thousands were literally dragged kicking and screaming to the desks. It was a sad time; the open outcry world was gone. After a few weeks in the basement of a building just yards from the playground of LIFFE, I sat staring at the deadly silent
ANALYST FOCUS screen. I saw numbers and prices but it didn’t make any sense - not like it had just weeks earlier. It was definitely a rabbit in the headlight’s scenario; my Noise Analysis had been stripped away and I needed to find an edge, and quickly. Revenues had plummeted and costs had spiralled. It was a case of re-training and the only real edge I saw was Technical Analysis (TA). This started to work once I had ironed out the over-trading. I was now using the same tools as a bank trader although he had deeper pockets and a bottom drawer that wouldn’t be opened for another 10 years. Time for a toupee? It wasn’t easy and the environment wasn’t as fun but TA continued to provide enough opportunities to make a living. The financial crisis of 2008 had a huge impact on this type of trading and competing with algorithms became harder and harder (their speed had picked up somewhat). The opportunity to join PIA First in 2012 felt like the right move for me at a time when risks from full-time trading no longer suited my risk appetite. Life events can change your approach and attitude, and this was a sea change for me. Within a year I was involved in a management buyout and was now invigorated having fallen back in love with the markets with a little help from my friend and long-term colleague to be - Ian Coleman. Technical Analysis was his passion, and he is one of the best I have come across. The next few years allowed us to focus on the technicals and we even decided to risk our reputation by taking the STA diploma! It would have been embarrassing had we not achieved a pass but thankfully it was not the case - I even managed to gain a distinction! (pictured above) Obviously down to my Dad's love of numbers! Bald is beautiful In 2018, we went for a name change to Signal Centre, trying to provide a little clarity on what we do... and also meaning we would no longer have to answer the common question ‘What does PIA stand for?’ We became authorised and regulated by the FCA in 2018. We wanted to provide peace of mind to prospective clients that we were a sound and reputable firm with a passion for clients’ success and hopefully we have done that. Ian has moved on to new pastures and my new business partner, Joe Neighbour, brings a fantastic skillset in wealth management and innovative product design. Joe and I face some big challenges and are ready to disrupt the areas that we specialise in.
Time to follow in the footsteps of Beckham and Rooney and get a hair transplant because I’m worth it? We have recently been acquired by Acuity Analytics, who specialise in creating News Sentiment Analysis tools. We are very excited about the future and we’re working on creating some very exciting and innovative products that combine multiple forms of analysis (Oh yes the F-word is welcome now). You’ll see the end of what you knew as ‘Talking Bull’ but we’ll be back better than ever with our new offering ‘Open Chart Surgery’, we’ve had a great time collaborating on the idea and playing with creatives so it will be well worth checking out. If I were to take a look at my career as a chart, it feels that I am moving towards the all-time highs! Is it a bubble??? I certainly hope not! A bullish engulfing will do... I am very excited for what is to come! Email: firstname.lastname@example.org
Interview with Deborah Owen FSTA, former STA Chair by Nicole Elliott, FSTA
Continuing our series of interviews with leading technical analysts, I have at last managed to pin down Deborah Owen (pandemic, quarantines and travel restrictions got in the way). With a light-hearted peek into the work routine and life of people well known in the industry, we aim to educate budding professional and amateur technical analysts.
Aping the format in national newspapers, which over the years have done ‘a day in the life of’, ‘if they could see me now’, and ‘my coat of arms would be’, our goals are more modest. We focus on the analysis but obviously educational background, business conditions, and age all have a role to play. We hope you enjoy this piece and feel free to put forward names of your heroes and heroines. Q1: Who/what introduced you to technical analysis? Elli Gifford; I had just started working as a financial journalist for Euromoney magazine and was sent off to a lunch that Elli (who was at the time Director of Research at Rudolf Wolff) was arranging for five or six fund managers. As with any lunch that Elli organised, it was both stimulating and enormous fun. However, at the end, when asked by Elli what I thought of the occasion, I had to confess that there were large parts of the discussion about the markets that had not made much sense to me. “Head and shoulders” and, even more intriguingly, “double bottoms” had not featured in my undergraduate course in economics. She then took me under her wing and in the weeks and months that followed essentially gave me a series of private tutorials in technical analysis. Elli was one of the main torch bearers of what was then still a relatively nascent form of analysis. Towards the end of her life, Elli had some underlying health issues but her death in 2004 was completely unexpected. I was very lucky to have had lunch with her a few weeks before she died in which I was able to express my gratitude for all her kindness and help in introducing me to the world of technical analysis. (NE: I too got to grips with the subject thanks to the fantastic David Fuller, who sadly died recently). Q2: Who else has been influential in shaping your views on technical analysis. What are your essential methods? Robin Griffiths has been important in shaping how I use technical analysis in my day-to-day professional life. Like me, he is an economist by training. Traditionally, technical analysts and economists formed completely separate camps, with the two sides taking every opportunity they could to disparage the other's methodology. In our book Mapping the Markets, Robin and I explained how both
ANALYST FOCUS disciplines have their roots in cycle theory and are essentially looking at cyclical patterns in the markets from different ends of the same telescope. Looking at long term cycles gives one a horizon against which one can steer investment strategy while technical analysis is a very effective shorter term management tool for defining tactics. The technical indicators that we tend to use at Investment Research of Cambridge [IRC] (NE: where Deborah has been its Managing Director for many years) are trend lines, pattern analysis, relative strength, momentum indicators and IRC’s version of the Coppock indicator.
inputting long run data series which predate this emphasis on algorithmic trading and therefore their ‘learning’ is still influenced by those human characteristics. Gradually, over time, the influence of the behavioural emotions will fade and be replaced by an analysis of more simplistic up and down movements. It will be interesting to see whether the predictive power of patterns and other indicators hold good in this environment. Q5: What’s the best piece of advice that you were given and what advice would you give to someone starting on a career in technical analysis today?
I would also like to pay tribute to my former colleague, Richard Marshall, who has now retired. He was quite simply the best ‘pure’ technical analyst that I have come across. Perhaps his most perceptive call was at the beginning of 2007 when he became increasingly concerned by the weak relative performance of the banking sector. At one investment seminar for our clients, he blanked out the title of the slide and asked them what they thought the asset was. No-one correctly identified the banking sector but, as a result of that slide, many of them reduced their holdings of financial companies significantly. As the enormity of the financial crisis unrolled, they were immensely grateful to Richard.
To learn from your mistakes. I think that is as true now as it was when I started working in the markets. (NE: I’d add that this advice is a transferable skill in most aspects of life; certainly one to remember). There are very few positions for pure technical analysts in the industry nowadays and I suspect that this will remain the case. I do, however, think it is important for anyone contemplating a career in finance to have a working knowledge of the concepts behind technical analysis.
Q3: Have events over the past 12 months altered the way that you analyse the markets?
It is invidious to restrict the choice to two books - the radio programme Desert Island Discs gives their interviewees eight discs! However, I’ll make three suggestions.
Yes, they have. The cyclical rhythms of the market, which have been in place for centuries, were first of all distorted by the quantitative easing measures introduced after the 2008/09 financial crisis and then completely up-ended by the tsunami of liquidity poured into the markets by the authorities in response to the Covid-19 pandemic. As a result, we have been putting more emphasis on technical indicators to navigate our way through the markets. Q4: What areas of research are you interested in, or might you suggest? I am currently working with Professor Riccardo Rebonato on developing a stress-testing model. It uses Bayesian nets and conditional probability to bring greater coherence and rigour to asset managers’ investment strategy. (NE: I should add that her academic credentials are strong, having been a Visiting Professor at Queen Mary, University of London and reader at Kings College, London.) Another area of research that interests me is the extent to which technical analysis and behavioural finance will remain relevant in analysing markets when all trading is carried out by artificial intelligence programmes. It is estimated that in the US, Japan and Europe, AI systems account for 70-80% of trading [volume] while it is still much lower in the emerging markets. Technical analysis is designed to capture the human emotions of fear and greed. Models are ‘trained’ by
Q6: Which are your two favourite books on technical analysis?
Without any doubt Charles Mackay’s Extraordinary popular delusions and the madness of crowds would be my first choice if I were to be castaway. It is a very powerful reminder of one of the central tenets of technical analysis - history repeats itself. I use John Murphy’s Technical Analysis of Financial Markets as my core reference book. It was first published in 1997 and the fact that it is now in its 21st edition is testament to it being a timeless classic. Despite its name, Computer Analysis of the Futures Market by Charles Le Beau and David Lucas is a very clear and coherent introduction to implementing technical analysis into your trading strategy. (NE: A new one for me, and hopefully a fruitful area for my own research). In conclusion: Deborah’s contribution to the Society of Technical Analysts has been enormous, stretching over many years. As former editor of this magazine, she has selected worthwhile pieces of research for inclusion, and encouraged writers specialising in finance. For five years she was chairman of the STA and, last but not least, while head of education was responsible for the STA’s diploma being recognised by the Chartered Institute of Securities and Investments (CISI). With so many arrows to her fabulous quiver, no wonder she’s a catch.
BOOK & SOFTWARE REVIEW
The Mental Game of Trading : Jared Tendler Book review by Alistair Philip, MSTA
This book offers unique perspectives on the psychology of trading and a step-by-step system for readers to follow. As a guide for a deep dive into personal psychology, it helps identify and explore what might drive individual behaviour in the trading arena. The explicit goal is to help you discover the causes of any issues in your trading and to eliminate them once and for all.
If you are unfamiliar with the author, Jared Tendler, (right) here is a brief introduction. He was a competitive collegiate golfer that continually choked in major national events. In a desire to understand the phenomenon of choking, he was driven to pursue academic studies and then professional qualifications in psychology. The Mental Game of Trading: Jared Tendler
“Early on the book discusses why the word “game” is so appropriate when it comes to what the trader is dealing with when approaching the mental side of trading.” Alistair Philip, MSTA
He has since coached clients in the world of sports, including esports, poker and trading. Tendler has written two previous books: The Mental Game of Poker and The Mental Game of Poker 2 and it was these books that first brought him to my attention some years ago. This new book represents his first mass-market foray into the trading world. Intuitively there is a lot of overlap between the professional poker world and the trading world, so the progression does make sense. The author assumes that you already have an approach, a system or an underlying set of technical skills that should permit you an edge in the market. That is an important note. It is widely accepted that psychology (or the mental game) plays a significant role in the potential success of any trader, but sometimes its importance can be overstated. That
said, it might often be the thing at the margin that enables you to reach the next level of performance. Early on Jared discusses why the word “game” is so appropriate when it comes to what the trader is dealing with when approaching the mental side of trading. The reason: it infers rules and a strategy for how you improve vs a one-and-done solution. The implication is that improving your mental game is an ongoing dynamic endeavour, especially in an environment that is constantly changing itself. One of the main ideas embraced from the get-go is that emotions are not in and of themselves a problem, but rather a signal (a technical indicator if you like) that will point you towards the underlying problems that exist. It is superficial to just deal with any emotions driving behaviours that ultimately adversely impact your trading results. You might have success in the short-term, but if you want longterm success, you need to go deeper to understand and work through what is driving those very emotions in the first place. Interestingly, when you do drill down, you might find that it is precisely
BOOK & SOFTWARE REVIEW
www.jaredtendler.com technical problems that are the underlying issue, rather than psychological ones. A refreshing acknowledgement that comes up repeatedly in the book and re-emphasises how emotions can be used as a guide to focus in on what really needs improvement in your overall approach.
your mental game in check in real time. There is even an additional section to help you troubleshoot any areas where you feel like you have made a lack of progress.
The core of the system is to first map your pattern ie, journal your emotional reactions, then to identify the roots of the problem and then to correct the problem. The book then moves onto a deep dive into greed, fear, tilt (anger for nonpoker players), confidence and discipline. It dedicates an entire chapter to each. At times, these chapters were somewhat repetitive, especially when the prescribed mapping method is rehashed in full in each chapter, albeit slightly adapted for each emotion. However, if you stick with it, you will almost certainly find yourself having moments of clarity about the nature of these emotions and the specific aspects that resonate most on a personal level. • Here is one line that stood out (taken from the chapter on fear): Aim for perfection; don’t expect it. Mistakes are inevitable. The goal is to learn from them faster. • Here is another (taken from the chapter on confidence): Recognition (of emotions) does not equal control. • And one more (taken from a later chapter on troubleshooting): If you didn’t do the work, you need to. There’s no way around it. The book wraps up with an outline of how you might keep
It flows best when the author introduces real world examples of the issues that his clients have faced and how they deployed the methods outlined in the book to make progress in their endeavours to improve their trading performance. These stories bring the system to life and offer anecdotal evidence that it has made a difference. The Mental Game of Trading explores a system that all traders will find useful to some extent. However, it is likely to be most useful for a trader who is involved actively and in close contact with the markets, having to make decisions on a regular basis. Importantly, as Tendler points out, reading it and expecting changes to come through osmosis is not going to cut it though; you have to do the work.
BOOK & SOFTWARE REVIEW
Bytes and Pieces David Watts MSTA Bsc(Hons) CEng MICE MIWEM MSTA Systems and Website Specialist
Classic long forgotten texts of Technical Analysis It’s now over 20 years since the Financial Times/Prentice Hall published a series of classic books of technical analysis with the introductions and editing by Don Mack, then a member of the STA. Don owned the Investment Book shop in California for many years before coming to England. Over 20 years of collecting, he obtained many of the early classics of Technical Analysis. The diversity of approaches and theories of markets make this series worthy of wider appreciation. Generally, only two or three of the early authors are recognised amongst these being Elliott, Schabacker and WD Gann.
programming has become increasingly popular. Advance GET is now part of the stable of products from Esignal. Then Trading Technologies has gone on to offer refined Elliott Wave. Automated Elliott Wave Analysis programmes are appearing thick and fast. Wavebasis (below) is the latest online subscription service providing automatic Elliott Wave counts and scanning. Wavebasis is challenging the now market leader Motivewave, which has been a go-to trading tool for many years.
Many of these authors are available via the Barbican Library and secondhand editions of this series may still be purchased. So, amongst those worthy of further study are:• Cole, George Graphs and Their Application to Speculation (1936). • William Dunnigan New Blueprints for Gains in Stocks and Grains (1954). • Pickell & Daniel Extension course for trading Commodities. • Tubbs, Frank - Tubbs Stock Market Correspondence Course. (1920s). • Wetsel Market Bureau Inc - A Course in Trading. • George Wollsten Expert Stock and Grain Trader by George Bayer.
Meanwhile the standalone programme by ELWAVE is still available. It would be amiss not to mention Elliott Wave International and their announcement last year that they were developing an Elliott wave programme or Glenn Neely's work with an automated approach called New River Trading. VisualTrader 12.5 Released
Not to forget Elliott
Nirvana, long-time developers of automated trading systems, has just released its “Relative Strength” (Not RSI but Relative Sector Analysis) trading tool Visual Trader 12.5 Upgrade, now with advanced scanning and automated reversal/breakout strategies included. Nirvana tools are becoming increasingly sophisticated and have become some of the best automated strategies available anywhere. Both Omnitrader with its strategies and VisualTrader 12.5 are amongst the best automated tools available Many of the systems have now real world equity curves having been developed many years ago.
The most used Technical Analysis map is probably that derived by Elliott and his wave counts. Since Trading Technologies first automated wave counts with their Advanced GET programme, a classic of its time, Elliott wave
Also see: Omni Trader Visual Trader Rocket Trader (Nirvana Systems)
I might add to this list Richard W Schabacker's book Technical Analysis and Stock Market Profits. The book is said to have inspired Edwards and Magee's book Technical Analysis of Stock Market Trends. Modern Technical analysis is often built upon the core themes from these early books.
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.
STA Calendar 2021 / 2022
More information about the STA events can be found here
See pg.4 for more info
TUESDAY 12 SEPTEMBER 2021
THURSDAY 27 SEPTEMBER 2021
FRI 8 & SAT 9 OCTOBER 2021
6.30pm, Via Live Webinar Denise Shull, The ReThink Group
STA ‘Freedom’ Party National Liberal Club
IFTA’s 34th Annual Conference (online)
TUESDAY 12 OCTOBER 2021
THURSDAY 21 OCTOBER 2021
TUESDAY 9 NOVEMBER 2021
6.30pm, One Moorgate Place Anthony Cheung, Amplify Trading
STA Diploma Part 2 Exam (online)
6.30pm, Via Live Webinar Speaker to be confirmed
MONDAY 6 DECEMBER 2021
TUESDAY 14 DECEMBER 2021
TUESDAY 11 JANUARY 2022
STA Diploma Part 1 Exam (online)
6.30pm, One Moorgate Place AGM and Christmas Party
6.30pm, One Moorgate Place Joint STA/ACI Market Outlook Panel
WEDNESDAY 12 JANUARY 2022
TUESDAY 8 FEBRUARY 2022
MONDAY 7 MARCH 2022
STA Diploma Part 2 Course (online)
6.30pm, Via Live Webinar Speaker to be confirmed
STA Diploma Part 1 Exam (online)
TUESDAY 8 MARCH 2022
TUESDAY 12 APRIL 2022
THURSDAY 21 APRIL 2022
6.30pm, One Moorgate Place Speaker to be confirmed
6.30pm, Via Live Webinar Speaker to be confirmed
STA Diploma Part 2 Exam (online)
The Education Channel Year
Panel debate with ACI UK
2021 mid-year outlook
Trading an adapted Gann Theory in the 21st Century
New Thought on Market Profile: Trading the Flow of Control
Long-term forecasting using IPA, Interdisciplinary Price Analysis
In conversation with Alistair Philip
Support and Resistance - Hidden Gaps and Other Methods
Panel Debate with ACI UK
In conversation with Steven Goldstein
Bitcoin: digital gold or a speculative growth asset
Dr Ernest Chan
Tail hedging the age of machine learning
The Education Channel: Monthly meetings videos are available to members www.technicalanalysts.com/meetings
STA Library STA UK members are eligible to join the Barbican 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
Patricia Elbaz catches up with Axel Rudolph, head of the STA Education Axel it looks like it’s been a busy year for the STA switching from face-to-face courses to online education. How have the students adapted to the changes? Patricia, yes the STA education committee has indeed been very busy, especially last year at the start of the pandemic when we very early on realised that we wouldn’t be able to teach our lectures in situ anymore. Our lecturers’ and students’ health has always been at the forefront of our minds. We thus spent the whole summer of last year testing online teaching and especially online Zoom invigilated examinations which, I am pleased to say, worked out extremely well. We were the first IFTA society to offer online invigilated examinations and had such a smooth transition from in situ to online teaching and examinations that we decided to stick to this format of teaching/examinations for this year as well. Patricia Elbaz Patricia Elbaz is an independent Technical Analyst and University Lecturer, specialising in the FX, Equity and Commodity markets. She was Foreign Exchange Manager Technical Analysis at MMS Standard & Poor’s before moving on to consulting work... Read more
The STA has introduced a new foundation course at Queen Mary University London (QMUL) this year. Can you tell us more about the course and the response from the students? The education committee felt that many students at the several London universities we teach at had a hard time sitting the STA Diploma Part 1 examinations on top of everything else they had to deal with in these unprecedented times. Furthermore, since only very few students historically have gone on to sit the STA Diploma Part 2 examination we believed that a “Foundation in Technical Analysis” course, examination and certificate would benefit university students more. With this in mind we streamlined the course taught at QMUL and recreated an entirely new multiple-choice examination data base. I am pleased to say that the new one-hour open book examination went extremely well. A record number of students sat the new STA Foundation Exam in Technical Analysis and the pass rate has increased to around 84%, on par with that of nonuniversity students who sat the STA Diploma Part 1 Examination. The QMUL post-graduate students stated that the STA course added a lot of value to their course and the university would like us to continue to work in the new format.
Axel Rudolph Axel Rudolph is Head of Education of the Society of Technical Analysts, and former STA Chairman (2013 to 2018). Prior to becoming chairman he was responsible for education and program organisation at the STA for six years. He has been a lecturer on the STA Diploma Part 1 and 2 courses at the London School of Economics and at Queen Mary University of London for nearly two decades. From 2007-2010 Axel was Vice-Chairman Europe on the board of the International Federation of Technical Analysts (IFTA)... Read more
Overall, how have the results been for the Part 1 and Part 2 exams? Interestingly enough, the examination results have been above average for those who sat our first online examinations last year, perhaps because those students who didn’t postpone their examinations because of the pandemic were the best prepared? The following examinations were mostly in line with previous examinations over the years, that is to say a few merits and distinctions and an overall pass rate in the 75%-80% region. Lastly, please tell us about the courses coming up in 2021/2022 and future educational projects. Since we constantly improve our courses - taking into account students’ feedback -
THE STA and because STA Administrative Services have done a lot of work perfecting the whole online experience for lecturers and students alike the 2021/2022 courses should be our best yet, especially since they can be done by anyone, anywhere in the world! We are, for example, pleased to have Constance Brown from Aerodynamic Trading, a world renowned Gann specialist, multiple book author and IFTA education committee member, teach our Gann lecture in the upcoming STA Diploma Part 2 Course. Connie has also kindly rewritten the Gann unit in the STA Home Study Course (HSC) (please add link), including her latest work. Future educational projects are to teach at several more UK universities, create a third level STA Course and Examination which focuses on systematic trading and continue to update and refresh the STA HSC, thus keeping it cutting edge. Thank you, Axel and congratulations to all students who were successful in the Foundation Course, STA Part 1 and Part 2 exams. We look forward to welcoming many more in the future. To browse all the STA courses, check the website www.technicalanalysts.com
Get qualified in technical analysis Booking well underway for the CISI and IFTA accredited online STA Diploma Part 1 and Diploma Part 2 courses. The two courses have been designed to cater for newcomers and experienced professionals who are looking to challenge themselves. They will learn to develop the methodology, tools and confidence to make better informed trading and investment decisions in any asset class, anywhere in the world. www.technicalanalysts.com/education
Special Journal Offer! We have put together a great offer for you. Book onto any of our courses, including the Home Study Course, before 31st October 2021 and save £50. Click here and enter code 'JNLPROMO' in the coupon box to redeem your £50 discount.
STA Diploma Part 1 Course
The course takes place from October to December each year. Delivered online via live Zoom, lectures are held once a week, from 6.00pm to 7.30pm London time. The 2021 course will start on Wednesday 13 October 2021. It costs £1,195 if booked by 29 September; £1395 thereafter. This course is designed for those with little or no previous experience and individuals looking to initiate themselves in the practice of technical analysis. The course will give you an introduction to technical analysis and provide you with the tools to progress to the Diploma Part 2 Course. The Diploma Part 1 schedule enables you to maximise your learning while complementing your work and home life. The course is accredited for Continuing Professional Development (CPD) by the Chartered Institute for Securities and Investment (CISI). Programme at a Glance • Introduction to technical analysis and comparison to fundamental analysis. • Construction and interpretation of Line, Bar, Point and Figure and Candlestick charts; introduction to Heikin- Ashi, Three-Line Break, Renko and Kagi charts. • Support and resistance, theory, identification, utilisation, breakouts. • Trend and return lines, where and how to draw them. • Fibonacci numbers and retracements. • Reversal and continuation patterns, target projection from patterns. • Moving averages, different types and how to interpret them. • Momentum, indicators/oscillators, relative strength, sentiment measures; definition, interpretation and how to use them. • Dow Theory, introduction to Elliott Wave Theory - how
6x1 evening a week classes 1 evening exam preparation session 2 hour exam Qualification accredited by CISI and IFTA
to use technical analysis strategically. Lectures are delivered via live Zoom webinar and are fully interactive with students being able to ask questions as they would in a classroom. Any students unable to watch live will be able to catch up with a recording post event and email the STA office if any questions. They may also post questions on the STA Student Forum which will be answered by course lecturers. Students are able to gain access to lecture recordings for the duration of the course. Dates for 2021 course are: • • • • • • •
Wednesday 13 October; Wednesday 20 October; Wednesday 27 October; Wednesday 3 November; Wednesday 10 November; Wednesday 17 November; Wednesday 24 November.
The Diploma Part 2 Exam will take place on Monday 6 December (daytime).
“Course was informative and enjoyable. The lecturers were subject matter experts and knowledgeable.” Matt Fitt, Shell, Student on the Diploma Part 1 Course, 2020
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STA Diploma Part 2 Course
12x1 evening a week classes Exam preparation video & guide booklet Three-hour exam Qualification accredited by CISI and IFTA
The course starts in January of each year and consists of 12 Wednesday online evening lectures (from 6.00pm7.30pm London time. The STA Diploma Part 2 exam is held in April. The 2022 STA Diploma Part 2 Course will commence on Wednesday 12 January 2022 and costs £1995 if booked by 30 December 2021; £2,995 thereafter. The Part 2 Course provides you with advanced professional knowledge, understanding and skills to use technical analysis as a vital investment tool or to pursue a career in technical analysis within the investment community. Basic technical analysis knowledge is a prerequisite for attending this course. During the 12-week programme you will learn from leading experts and develop both theory and practical experience in the major techniques, analytical tools and indicators to enable you to select the most advantageous portfolios, trades, hedges and much more for your clients, your employers or your own trading systems. The Diploma Part 2 Course provides you with a deeper understanding of technical analysis, added confidence and the capabilities to further develop your career. The course is accredited for Continuing Professional Development (CPD) by the Chartered Institute for Securities and Investment (CISI). Programme at a Glance • • • •
The practical application of support, resistance and price objectives by market professionals - how they build on the essential basics and add advanced techniques e.g. Fibonacci projections; working in different time frames Construction and advanced applications of Candlestick and Point and Figure charts, including Point and Figure moving averages and indicators. Advanced moving average, momentum indicator and oscillator techniques; use of market breadth and sentiment measures. The practical application by market professionals of Dow, Elliott Wave and Gann Theory; Ichimoku Charts; Market Profile®; Behavioural Finance; Risk Management - and much, much more.
be able to catch up with a recording post event and email the STA office if any questions. They may also post questions on the STA Student Forum which will be answered by course lecturers. Students are able to gain access to lecture recordings for the duration of the course. Dates for the 2022 course will be as follows: • • • • • • • • • • • •
Lecture 1 (Wednesday 12 Jan); Lecture 2 (Wednesday 19 Jan); Lecture 3 (Wednesday 26 Jan); Lecture 4 (Wednesday 2 Feb); Lecture 5 (Wednesday 9 Feb); Lecture 6 (Wednesday 16 Feb); Lecture 7 (Wednesday 23 Feb); Lecture 8 (Wednesday 2 March); Lecture 9 (Wednesday 9 March); Lecture 10 (Wednesday 16 March); Lecture 11 (Wednesday 23 March); Lecture 12 (Wednesday 30 March).
The Diploma Part 2 Exam will take place on Thursday 21 April (daytime).
“High level of lecturers who make the experience extremely qualitative.” Maxime Lelong, EDF Trading, Student on the 2021 Diploma Part 2 Course
Lectures are delivered via live Zoom webinar and are fully interactive with students being able to ask questions as they would in a classroom. Any students unable to watch live will
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Balance professional development and your personal life with the STA Home Study Course© WHY PURCHASE THE HOME STUDY COURSE? The world-class e-learning Home Study Course (HSC)© is written by leading industry practitioners, making it one of the best online products available on the technical analysis market. Whether this is your first introduction to technical analysis, you want to refresh your existing knowledge, or you wish to become a qualified technical analyst, the STA offers a tailored Home Study Course as part of our portfolio of world respected courses preparing students for our internationally accredited STA Diploma qualification. You can learn from the comfort of your home at times that best suit you. Although website based, it is fully downloadable and may be used online or offline via PC, Mac, iPad or Android machines. WHAT WILL IT COVER? • The syllabi for both STA Diploma Part 1 & Part 2 examinations • 15 in-depth subject teaching units • Exercises to self-test progress • Exam preparation module & video • Advice on report writing. ...find out more here
Since the HSC is International Federation of Technical Analysts (IFTA) syllabus compliant it can also be used to prepare candidates for both the IFTA CFTe I and II examinations. WHO IS THE COURSE FOR? The course is intended for individuals who want to use technical analysis in a professional manner or who want to become a qualified technical analyst and advance their career. Enrol and start studying now! For more details click here or contact the STA office on +44 (0) 207 125 0038 or email@example.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
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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.
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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 firstname.lastname@example.org or +44 (0) 207 125 0038 for more information.
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The UK’s professional body for Technical Analysts. Founded in 1968. The oldest of its kind in the world.
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