Editor's Letter
Welcome to Issue 99
of Market Technician, the journal of the Society of Technical Analysts.
As we approach the milestone of our 100th edition, it’s striking how this issue captures the breadth, energy and evolving sophistication of our discipline. The world of technical analysis continues to balance historical wisdom with forward-thinking innovation.
This edition offers clear evidence of that progression.
Sankar Sharma MSTA (pg.12) opens the journal with an in-depth exploration of the Cup and Handle pattern - a formation familiar to many but here presented with fresh precision and strategic depth. This analysis serves as a reminder that even the most recognisable patterns can yield new insights when approached with discipline and a systematic mindset.
Karen Jones FSTA (pg.21) contributes a powerful critique of the Efficient Market Hypothesis, combining her own experience with behavioural finance and a nuanced understanding of market dynamics. Her work challenges long-standing academic assumptions and reinforces the practical, real-world value of technical analysis in today’s environment.
Priya Mittal and Sasha Medin’s (pg.24) quantitative study on candlestick patterns spans four decades and over a hundred million data points.
Their rigorous research offers a fascinating look at how the effectiveness of familiar signals has evolved - or in some cases been eroded - in response to changing technologies, market structures and trader psychology.
The announcement of London as the host city for IFTA 2026 marks an exciting moment for our organisation.
This issue also reflects the vibrant personality of our STA community.
We are delighted to welcome Michael Hewson MSTA (pg.35) to the Board: in conversation he shares thoughtful reflections on his journey, the tools that shaped him and the lessons learned over decades of market engagement.
The announcement of London as the host city for IFTA 2026 (pg.11) marks an exciting moment for our organisation and we are bringing together a truly international group of technical analysts to share knowledge, insights and connections.
As ever, our STA courses (pg.42) continue to grow in impact, helping experienced and new professionals
to formalise their learning and build confidence in their analytical approach.
As you read through this issuewhether you’re revisiting foundational techniques or exploring the latest research - I hope you feel the same sense of energy and dedication that defines the STA and its members.
This journal exists not only to inform but to celebrate the craft of technical analysis and the people who carry it forward.
Thank you for reading.
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MOU renewal with CISI
The STA and Chartered Institute for Securities & Investment renew Memorandum of Understanding
On Tuesday the 8th April, our Chair, Eddie Tofpik, renewed our Memorandum of Understanding (MOU) with the Chartered Institute for Securities & Investment (CISI), continuing a long-standing collaboration focused on a shared passion for professional education and capital markets.
We are looking forward to more insightful events and finding other ways to collaborate for the benefit of our members.
STA on social media STA Chapter Ireland
Don’t forget that the STA is active on LinkedIn, X , Facebook, Instagram, YouTube, Spotifyand even TikTok!
Across these platforms you can keep up with the latest market news, member activities, interviews and watch short educational videos with STA’s content editor, Karen Jones - former Managing Director, Commerzbank.
Click on the icons to find us and follow!
Irish Chapter Hosts Insightful FX Market Networking Event
On Thursday 17 July, the STA’s Irish Chapter hosted a networking event at The Westbury Hotel in Dublin, generously supported by CMC Markets
The evening featured engaging presentations from guest speakers Alan Dunne, CEO of Archive Capital, and Patricia Elbaz , independent Technical Analyst and lecturer, who shared their expertise on the FX markets before members and guests gathered for a networking reception. Alan opened the session with a concise overview of the current FX landscape, outlining the key macroeconomic themes, primary market drivers, and the ways in which the market has evolved in recent years.
Patricia followed with a thought-provoking talk that explored the origins and enduring relevance of Dow Theory, its influence on FX trading, and the significance of crowd behaviour, market psychology, and the distribution phase. She concluded with a forward-looking discussion on the practical application of Dow Theory in today’s FX and equity markets, with particular attention to developments anticipated in 2025.
Congratulations to Quiz winner John Sweeney, who identified the Silver Chart and went home with a signed copy of Patricia’s book on Dow Theory in French, his reaction ‘I’m grateful for google translate!’.
Market discussion continued over a reception which was generously sponsored by CMC Markets. Look out for our next event and an opportunity to network during the reception!
Why I Became (and remain) a Member of the STA
Reflections from an STA Board Member
The Society of Technical Analysts (STA) recently conducted a membership survey. Given my role as a board member, I didn’t feel it was appropriate for me to take part but it did make me stop and reflect. Why did I join the STA in the first place? Why do I remain a member? And why have I committed to serving on the board?
Starting Out
I began my career in the city as a junior technical analyst. It didn’t take long to realise how little I knew. Technical analysis was fascinating - full of potential - but I lacked a proper foundation. I had to teach myself and fast. More importantly, I had to understand how others were actually using technical analysis in real-world trading.
Over time, I noticed a clear trend: successful traders were using technical analysis not just to place trades or spot profit opportunities, but - crucially - to manage risk. They were using it to decide where to place stops, how to avoid big losses and how to approach the market with discipline. That insight shaped the rest of my career.
Becoming an Educator
As I developed, I found myself drawn to education. I wanted to help others understand how technical analysis could be used not just to trade, but to trade well - with a plan, with purpose and with a strong grasp of risk management.
But this raised an unexpected challenge: I had become so fluent in the language of technical analysis that I’d lost touch with how to teach it. I was skipping over the basics because they had become second nature to me. That’s when I realised that I had to go back to school myself.
Rediscovering the Basics
Attending STA monthly seminars helped me reconnect with the fundamentals. I re-learned concepts like Fibonacci retracements and how to choose the right RSI settings - tools I’d long used instinctively. Revisiting these basics reminded me just how important they are and how crucial it is to be able to explain them clearly to others.
The STA provided that structure. It helped me verbalise what I’d previously just done automatically. Just as importantly, it gave me a network - a community of like-minded professionals who are as committed to learning and sharing as I am.
Lifelong Learning
Even now, years into my career, I’m still learning. Recently, Stephen Hoad introduced me to Renko charts (pg.37)something I hadn’t used much before. It was a reminder that in technical analysis there’s always something new to discover.
That’s one of the biggest advantages of STA membership: access to a community of experts who are not only deeply knowledgeable but incredibly generous with their time and insight. The willingness of STA members to share what they know openly and freely is what makes this organisation so special.
Why I Stay
So, why am I still a member? Because the STA continues to offer value - education, connection, inspiration. And why am I a board member? Because I want to give back. I want to ensure others have access to the same opportunities for growth that I’ve had.
If you’re on your own journey with technical analysiswhether you’re just starting or have decades of experience - I can’t recommend STA membership enough. It’s not just about learning charts. It’s about joining a community dedicated to improving the way we understand, teach and apply technical analysis.
For more information about membership to the Society please click here
KAREN JONES FSTA
“Over
time, I noticed a clear trend: successful traders were using technical analysis not just to place trades or spot profit opportunities, but - cruciallyto manage risk.”
KAREN JONES
International Technical Analyst Day 9/9
The Society of Technical Analysts will be celebrating International Technical Analyst Day again this year on 9 September.
Eddie Tofpik, the Chair of the STA, first launched this initiative in 2022. The idea was and remains to give technical analysts and those using technical and chart analysis at funds, banks, brokers, proprietary trading shops or for themselves, a day to get recognition for the hard work, dedication and diligence that they display through all market conditions and events.
We encourage colleagues, clients and counterparties to say, ‘Thank you!’ to their staff technical analysts and TA providers from around the world on this day. We are thrilled that this year it coincides with a special online fireside chat with legendary professional trader and consultant, Tony LaPorta who will be in conversation Clive Lambert FSTA, Founder of FuturesTechs. Clive and Tony first met working at the London International Financial Futures Exchange (LIFFE) back in the 90s.
Tony LaPorta (left) has been active in the futures industry for 46 plus years including membership at the Chicago Mercantile Exchange (CME) and the London International Financial Futures Exchange (LIFFE). Tony is an ex pit-trader turned screen-trader. He writes with deep insight providing well-documented and highly educational chart analysis in his daily financial market commentary. Through technical expertise and a keen ability to translate market indications and correlations, Tony identifies swing trades for his clients and himself. His years of experience and gut intuition is what separates Tony from the competition.
Starting at 6.30pm UK time, join us online in what promises to be a lively session as they recount trading stories and talk about trading ‘rules’.
Join the Fireside Chat with Tony LaPorta
ITAD 2025 with Eddie Tofpik
STA to host IFTA Conference 2026
We are delighted to announce that the STA will be hosting the 2026 International Federation of Technical Analysis (IFTA) conference here in London.
The two-day event will be held on Friday 9 October and Saturday 10 October 2026 at One Moorgate Place, London.
This conference will bring together the world’s leading experts in technical analysis, quantitative trading and financial markets, exploring cutting-edge strategies, modern applications, and emerging trends.
The theme running through the two days will be very much focused on TA in the age of AI.
With an exceptional speaker-line up, this will be a fantastic networking event, culminating in a Gala Dinner on the Saturday evening at Plaisterers’ Hall, City of London.
If you are interested in sponsoring or taking part in any way please do contact the STA office on info@technicalanalysts.com
Images from IFTA 2014. London
Precision Profits Using the Cup and Handle Price Pattern
A Strategic Framework for Capturing Big Price Moves Across Timeframes and Asset Classes
SANKAR SHARMA, MBA (FINANCE), MSTA
Keynote Speaker | Author | Trading and Investing Mentor
Founder, Pathway to Prosperity™ and RiskRewardReturn.com
Featured six times in Forbes and over 100 top financial media outlets
Member of STA and IFTA
This curious shape has been making money for my clients, customers, traders and for the students I have been mentoring. It would be a disservice not to share this with my fellow members of the STA, IFTA, and associated institutions.
It always begins with a peculiar curve on a price chart, a gentle, rounded dip, followed by a fierce downward pull. Most traders ignore it, branding it as ‘market noise.’ but this price formation offers a powerful and precise opportunity to capture big move profits before they happen.
Why does this strange teacup-like formation so often lead to explosive price moves and remarkable gains?
Is it just a storm in a teacup that boils over into a breakout? Or could it be due to crowd psychology, human behaviour,the rhythm of the market or is it a pretty shape with a hidden magic portion?
What makes the cup and handle pattern one of the most precise, powerful, reliable, and consistent profit-making patterns in technical analysis?
In this article, we’ll take a deep dive into:
i. The step-by-step strategy implementation; ii. Alternative ways to use the pattern for profits; iii. Tips to improve the probability of success; and iv. Real-world case studies that illustrate its power.
Next time you take a sip of your afternoon tea, consider whether this might just help you to lock in some precise profit moves.
If this work inspires you to start using the cup and handle pattern, drop me an email - I would love to hear about it.
If you haven’t tried it before, I encourage you to give it a go, and you might just capture the next big move in your favourite market before it happens!
Cup and Handle Price Pattern Strategy
Step-by-step Implementation:
Here is how to implement the Cup and handle strategy for profits.
• Identify the low end of the cup (marked as ‘1’ on the chart)
• Identify the high end of the cup (marked as ‘2’ on the chart)
• Join points ‘1’ and ‘2’, we get the Lip of the cup (marked as ‘3’, the red line)
• Join points ‘1’ and ‘2’ with the cup using a curved line (marked in purple)
• Get Measured move 01, from the lip to the lowest point (approximately)
• Project the Measured move from high lip node ‘2’ to get Measured move 02
• Draw a parallel channel from node 2 to the low-price pivot
• Enter the trade when the price breaks above the channel
• Take profits when the Measured move 02 is hit
• For stops, you can use either the bottom of the channel or the mid-level of the parallel channel
• PRO TIP: You could also use Fibonacci and volume for identifying stops!
Psychological and Behavioural Finance behind the Cup and Handle:
The psychological and behavioural patterns of the traders are unique during the Cup and Handle price pattern formation. This can be seen repeating itself and applies to all time-frame. During the Cup and Handle formation, the psychology goes through the three phases of Fear, Greed, and Confirmation Bias. We can see it in greater detail as follows:
• The lower end of the cup starts with ‘Fear’.
• During the cup, the ‘Fear slowly but surely turns into optimism'.
• During the handle, ‘Doubt creeps in, weak hands are shaken out’.
• During the breakout, ‘Euphoria sets in and momentum builds'.
Case Study 1: Commodity Trade SILVER
Below is a commodity trade example: iShares Silver Trust (NYSE: SLV). This is a demonstration of the explosive profits that a Cup and Handle pattern can capture.
If you observe the daily chart of SLV, this is a high-probability price breakout with a precise profit target. The reward is multiple times greater than the risk in the trade. The entry is triggered by the breakout of the parallel channel, the stop-loss is placed just below the channel, and the target is measured from the high point of the right lip of the cup.
A beautiful commodity trade.
Case Study 2: Commodity Trade GOLD
Below is another commodity trade example: Gold Futures (COMEX: GC1!) and anotherillustration of the explosive profits that a Cup and Handle pattern can capture.
Note the second green upward arrow around the $1600 level; this move resulted in an explosive breakout on the Monthly chart. This is not a move to laugh at or dismiss as market noise.
Since the chart we’re using is the Monthly timeframe, the move took some time to fully manifest but it ultimately met the target with precision
Case Study 3: QQQ Trade
Here is the Monthly chart of QQQ (NASDAQ: QQQ). We can see that the Cup and Handle target has been met. The entry was triggered by the breakout from the parallel channel and the measured move was comfortably achieved.
Remember that the target should be measured from the high point of the lip of the cup. In this case, that high point is on the left side of the cup (see the exact projection starting point from the left lip).
Had we measured the target from the right lip, we would have underestimated the potential move and left a significant amount of profit on the table. This is why it is essential to always project the measured target from the highest point of the cup’s lip.
Case Study 4: SPY Trade (SPY: NYSE)
Here is the Weekly chart of the S&P 500 ETF. We can see that the Cup and Handle target has been met. The entry point was triggered by the breakout from the parallel channel (marked by the black upward arrow) and the measured move was comfortably achieved.
As with the QQQ example, it’s important to remember that the target should be measured from the high point of the cup’s lip In this case, that point is on the left side of the cup (see the exact projection starting point from the high side of the formation).
Had we measured the target from the right lip, we would have significantly underestimated the move and left substantial profits on the table. This reinforces a key rule: always project the measured target from the highest point of the cup’s lip
Case Study 5: 4-Hour Chart of Bitcoin/U.S.Dollar
Here is a four-hour intraday chart of the Bitcoin/U.S.Dollar (BTCUSD). We can see that the Cup and Handle price pattern worked very well, even on a four-hour BTCUSD chart.
Again, it is important to remember that the target should be measured from the high point of the cup’s lip. In this case, that point is on the left side of the cup (see the exact projection starting point from the high side of the formation).
Entry in this case is the break of theHandle to the upside (channel breakout to the upside). Stop below the last pivot price. The target is the measured move from the bottom of the cup to the top of the lip.
The rules remain the same, whether it is Commodity, Forex, Stock, Index, Bitcoin, Crypto, Bonds, or any other asset class.
Pattern To Profit: A Trader’s Mindset
Here are two simple mindset hacks that can help you get better results. Exercise patience during the Cup and Handle formation and have the courage to join the price rally at the time of breakout.
Myths and Misconceptions
Traders were wrongly led to believe that Cup and Handle patterns work only in bull markets. Our research found this to be untrue. Traders were made to believe that this pattern works only with large-cap stocks. This is untrue. Cup and Handle works on all timeframes and works on all asset classes.
Alternative Cup and Handle Strategy
This is a simple variation which has been working very well for the past 4 years. In this variation, wait until the price breaks out of the handle range and use that as an entry (identified by the up arrow in the AMD – Applied Micro Devices chart). The target is the highest lip end of the cup (marked by the dotted line towards the top of the price chart). The stop is below the lower end of the handle, price pivot. Simple yes: but it makes money and works well consistently.
Pitfalls to Avoid While Using the Cup and Handle Pattern
While the Cup and Handle pattern is a powerful and elegant setup for capturing big-move profits, it is important to note that if the handle pullback is as deep as, or deeper than, the cup itself, the trade should be avoided.
Stacking Odds in Your Favour
While the Cup and Handle pattern alone is sufficient to determine entries and exits, you can further enhance your precision and confidence by combining it with tools such as Volume, Ichimoku or Fibonacci. Using these in conjunction with the Cup and Handle pattern can help you identify high-probability and well-timed entries and exits with greater accuracy.
Disclaimer: All forms of investing and trading involve risk; asset classes mentioned here are not a recommendation to buy or sell.
Conclusion
The Cup and Handle pattern is not a myth. It works consistently across all chart timeframes and in all types of markets However, it is important to remind ourselves that success doesn’t come from recognizing the pattern alone. To truly benefit from its potential, one must also master the discipline and strategy behind it.
Learn more about price pattern profits
Reassessing the Efficient Market Hypothesis: A Case for the Validity of Technical Analysis
Karen Jones is Vice Chair, Treasurer and Content Editor for the STA. She was formerly Managing Director and Head of FICC Technical Analysis Research at Commerzbank Corporates and Markets.
“We argue that technical analysis, when applied judiciously and in conjunction with behavioural finance insights, offers a valid and valuable approach to market analysis.”
KAREN JONES
The Efficient Market Hypothesis (EMH) has long been a cornerstone of modern financial theory, asserting that asset prices fully reflect all available information. According to EMH, technical analysis is futile, as price movements are essentially random and unforecastable.
This article challenges that assertion by critically examining the assumptions underlying EMH, empirical anomalies in financial markets, and the evolving nature of market dynamics. We argue that technical analysis, when applied judiciously and in conjunction with behavioural finance insights, offers a valid and valuable approach to market analysis.
Introduction
The EMH, particularly in its strong and semi-strong forms, posits that market prices instantly incorporate all available information, rendering any attempt to outperform the market through analysis redundant. This theoretical position has led many academics to dismiss technical analysis as pseudoscientific. However, real-world market behaviour frequently contradicts EMH assumptions, suggesting room for alternative analytical frameworks.
The Momentum Effect
One of the most well-documented violations of EMH is the Momentum Effect Stocks that have performed well in the past 3 to 12 months can continue performing well in the near future while underperformers continue lagging. This phenomenon directly contradicts the central EMH premise that all known information is instantly reflected in stock prices and that past price movements cannot predict future returns.
Seminal research by Jegadeesh and Titman (1993) demonstrated that momentumbased strategies yielded returns well beyond what could be explained by riskbased models. Subsequent studies confirmed these findings across various markets and time periods, suggesting that behavioural factors like investor overreaction and underreaction play a significant role - these factors are not accounted for by EMH.
The Theoretical Foundation of EMH
EMH rests on several key assumptions:
i. rational behaviour among investors; ii. homogeneous expectations; and iiii. instantaneous information dissemination.
However, these assumptions have been increasingly scrutinised. Behavioural finance has shown that investors often act irrationally due to cognitive biases,
KAREN JONES FSTA
herd behaviour and emotional influences. Additionally, information asymmetry and latency in dissemination challenge the notion of perfect markets.
Here is a strong example that illustrates the irrationality of investors and the limitations of EMH due to behavioural and informational factors:
Example: The Dot-Com Bubble (1995–2000)
During the late 1990s, investor enthusiasm for internetbased companies led to a massive speculative bubble in technology stocks. Many companies with little to no earnings - and sometimes not even viable business models - saw their stock prices soar simply due to market hype and herd behaviour.
Behavioural finance explains this through cognitive biases such as over-confidence, confirmation bias and herding (where investors mimic the actions of others, assuming the crowd must be right). Emotional drivers like fear of missing out (FOMO) further fuelled irrational investment decisions.
At the same time, information asymmetry and latency in understanding the real value of these companies allowed prices to detach dramatically from fundamentals. The eventual market crash in 2000 revealed how inefficient the market had been, directly undermining the EMH assumption that prices always reflect all relevant information rationally and instantaneously.
Empirical Anomalies and Market Inefficiencies
Numerous market anomalies contradict EMH. These include momentum effects, mean reversion, seasonal patterns (e.g., the January effect) and technical chart patterns that persist over time. Studies have shown that strategies based on moving averages, relative strength and breakout signals can yield statistically significant returns.
Technical Analysis as a Tool for Behavioural Insight
Rather than being a futile exercise, technical analysis can be viewed as a method for interpreting collective investor psychology. Patterns such as support and resistance levels, head-and-shoulders formations and volume trends often reflect underlying sentiment and decision-making heuristics. Technical tools, therefore, provide insight into market psychology that cannot be captured by purely fundamental or informational metrics.
The Adaptive Market Hypothesis and Modern Market Dynamics
Andrew Lo's Adaptive Market Hypothesis (AMH) presents a more nuanced view, suggesting that market efficiency is not static but evolves over time based on competition, adaptation and innovation. This framework accommodates periods of inefficiency which is where technical analysis can be particularly effective and advancements in
machine learning and algorithmic trading only enhance the sophistication and efficacy of technical strategies.
Practitioner Insight: Herd Behaviour and Market Turning Points
During my tenure as a professional technical analyst, I observed behavioural patterns on the trading floor that consistently contradicted the assumptions of the Efficient Market Hypothesis. There were frequent periods when nearly every trader held the same directional position on a particular asset—an alignment that, to me, signalled a potential reversal point. The logic was simple: if the majority of participants were already long, the question becamewho was left to buy?
These consensus-driven positions were not purely the result of objective analysis. Traders would openly discuss their positions. When someone held a contrarian view, they often faced subtle peer pressure to conform. There was a palpable social dynamic at play, driven by performance anxiety and the imperative to avoid losses. Traders are evaluated primarily on profit and loss. Fear of underperformance and the ego associated with being on the “winning side” led many to abandon their convictions in favour of the crowd.
I noticed that these behavioural cues became even more pronounced as price approached key technical levels. The alignment of sentiment and positioning at these junctures often preceded sharp reversals. Over time, I systematised
this observation into what I termed a “reversal watch” framework, integrating chart analysis with behavioural finance principles. This approach - grounded in both technical patterns and the psychological context of the market - provided a reliable contrarian indicator, highlighting the limitations of EMH in accounting for human behaviour.
This real-world experience aligns with criticisms of EMH from some of the most successful investors of our time. Warren Buffett and Charlie Munger, for example, have long been sceptical of EMH.
Buffett has noted that while the theory suggests consistent outperformance is impossible, his own success - and that of his mentor Benjamin Graham - offers strong evidence to the contrary. In a public discussion, Buffett compared the persistence of EMH in academia to believing “the world is flat” while running a merchant shipping business. “If all of your competitors believe the world is flat,” he quipped, “you have a huge edge.”
Munger added that the academic contamination from EMH has been “massive,” though he acknowledged that it is waning. He criticised the reluctance of economists to revise their theories when confronted with contradictory evidence, likening the dismissive term “anomaly” to rejecting inconvenient truths rather than re-examining flawed premises. In his view, the enduring belief in EMH despite overwhelming empirical refutation is more a product of intellectual inertia than robust scientific reasoning.
Notably, Munger once mocked a leading proponent of
EMH who tried to explain Buffett’s success as a six-sigma event of pure luck—statistically improbable to the point of absurdity. As Buffett’s consistent performance continued, the theorist shifted to calling it “six or seven sigma's of skill,” underscoring the lengths to which EMH defenders go to preserve the theory in the face of mounting contradictions.
These insights reinforce a crucial point: markets are neither fully rational nor perfectly efficient. Behavioural biases, crowd dynamics, and psychological influences all play roles in shaping price movements—factors that technical analysis can help interpret when used with discipline and insight
Conclusion
While EMH offers a valuable baseline for understanding market dynamics, its assumptions do not consistently hold in practice. Technical analysis, far from being a relic of pre-modern finance, provides a flexible and empirically grounded framework for navigating market complexity. By integrating technical analysis with behavioural and adaptive insights, investors can develop more robust strategies that acknowledge the nuanced reality of financial markets.
Warren Buffett and Charlie Munger licensed under CC by 4.0
The Candlestick Patterns Predictive Power
PRIYA MITTAL
Priya is a financial mathematician and research scholar at the Delhi Institute of Technology, specializing in convolutional neural networks.
Introduction
“We crunched the numbers across 40 years and 127 million bars.”
PRIYA MITTAL & SASHA MEDIN
Sasha is the creator of several data projects and the author of the statistical YouTube channel Data is Beautiful. He is also a hobbyist algorithmic trader.
We crunched the numbers across 40 years and 127 million bars.
This publication is the first in a series of studies we are conducting on the effectiveness of technical analysis in market trading. The next instalment will focus on the most commonly used technical indicators and the seasonality of market regimes.
Markets Change. Do Trading Patterns Keep Up?
We all know the classic candlestick patterns: the Hammer, the Doji, and the Engulfing patterns. They're staples of technical analysis, found in countless books and tutorials.
But here's a question we kept asking ourselves: do they actually work as well today as they did ten or twenty years ago? The market landscape has shifted dramatically. We've seen the rise of high-frequency trading, seismic shifts in retail participation, near-zero interest rates, meme stock frenzies and global crises.
It felt intuitive that signals which worked reliably in, say, 1998 might behave differently in 2025. Gut feelings aren't enough, though. So we decided to measure it.
SASHA MEDIN
Patterns We Analysed
Bearish Patterns
Hanging Man
Dark Cloud Cover
Three Black Crows
Shooting Star
Bearish Harami
Bearish Marubozu
Bearish Engulfing
Evening Star
Tweezer Tops
Bullish Patterns
Hammer
Piercing Line
Three White Soldiers
Inverted Hammer
Bullish Harami
Bullish Marubozu
Bullish Engulfing
Morning Star
Tweezer Bottoms
Measuring the Pattern Predictive Power
To quantify this evolution, we compared pattern effectiveness across four distinct market eras.
We defined four key periods:
Discovery and Early Adoption (Era 1): 1984 - 1995 Candlestick charting, while ancient in Japan, was being actively "discovered" and popularized in the West.
Democratization and Early Exploitation (Era 2): 1996 - 2007
The rise of the internet and online brokerages.
Algorithmic Dominance and Efficiency (Era 3): 2008 - 2019
Post-GFC, High-Frequency Trading (HFT) and sophisticated algorithmic strategies became dominant market forces.
Volatility, Retail Resurgence, and Complexity (Era 4): 2020 - 2024
The COVID-19 pandemic triggered extreme volatility and a massive influx of new retail traders.
Proposed Thesis
The Era of Discovery and Early Adoption (1984 - 1995)
Context: Candlestick charting, while ancient in Japan, was being actively "discovered" and popularized in the West (e.g., Steve Nison's books). Personal computers were becoming more common but real-time charting software wasn't ubiquitous or cheap. Trading was still dominated by floor traders and institutions; retail access was limited and slower. Information flow was significantly slower than today. The 1987 crash likely spurred interest in any technical tools that might offer predictive power or risk management signals.
Hypothesized Candlestick Effectiveness: Relatively High. Patterns were novel to the Western market. Fewer participants were actively looking for them or programming algorithms to exploit them. Market movements were perhaps "cleaner" in terms of human emotional response reflected over the course of a candle's formation, without highfrequency noise. The "edge" from recognizing these patterns was likely more significant.
Key Event Influence: Introduction of PCs; early charting software; the 1987 Crash; Nison's popularization of candlesticks.
The Era of Democratization and Early Exploitation (1996 - 2007)
Context: The rise of the internet and online brokerages (E*TRADE, Schwab, etc.) dramatically increased retail participation (Dot-com boom). Charting software became widely available and affordable. Candlestick patterns became a staple of retail technical analysis education. Early algorithmic trading began to emerge, initially focused on arbitrage and execution but increasingly incorporating pattern recognition. Decimalisation in the US (2001) changed market microstructure, potentially affecting very short-term patterns.
Hypothesized Candlestick Effectiveness: Moderate but decreasing. Increased awareness meant more traders were acting on the same signals, potentially leading to self-fulfilling prophecies initially but also making them predictable targets ("stop hunts" above/below key patterns). Early algorithms could identify and trade simple patterns faster than most humans. The sheer volume of retail traders might have amplified noise. Effectiveness likely started to erode, especially for the most common patterns on liquid stocks.
Key Event Influence: Dot-com bubble & crash; rise of online brokers; widespread charting software; US decimalisation; and early algorithmic trading growth.
The Era of Algorithmic Dominance and Efficiency (2008 - 2019)
Context: Post-GFC, High-Frequency Trading (HFT) and sophisticated algorithmic strategies became dominant market forces. Information velocity reached near light speed. Quantitative funds systematically mined data for any edge, including basic technical patterns. Regulatory changes (e.g., Reg NMS fully implemented) solidified electronic market structures. Central bank interventions (QE) often led to periods of suppressed volatility, potentially reducing the frequency or clarity of strong patterns. Passive investing via ETFs also grew significantly, changing intraday flows.
Candlestick Effectiveness: Generally Low. Simple, visually identified candlestick patterns were recognized and arbitraged away by algorithms. Predictive power was fleeting (milliseconds to seconds) or statistically insignificant when transaction costs were factored in. Patterns might still appear but their reliability as a standalone predictive tool for discretionary traders probably hit a low point. Effectiveness might have briefly spiked during sharp volatility events (e.g., Flash Crash 2010, Taper Tantrum 2013) but the baseline effectiveness was likely poor.
Key Event Influence: Aftermath of Global Financial Crisis; rise of HFT dominance; QE/ZIRP policies; Flash Crash; increased quant sophistication; and growth of passive investing.
The Era of Volatility, Retail Resurgence, and Complexity (2020 - 2024)
Context: The COVID-19 pandemic triggered extreme volatility and a massive influx of new retail traders (‘meme stock’ phenomenon), often co-ordinating via social media. Markets became highly narrative driven. While algorithms remained dominant, the sheer force of retail flows (sometimes irrational or meme-driven) introduced new dynamics. AI and machine learning became more integrated into institutional trading strategies, capable of recognizing far more complex patterns than traditional candlesticks.
Hypothesized Candlestick Effectiveness: This has been variable and context dependent (potentially low-to-moderate in specific niches/conditions). This is a complex area.
Against Effectiveness: Algo dominance and AI sophistication mean simple patternscan be weak in isolation on highly liquid stocks.
For Situational Effectiveness: Extreme volatility can create clearer emotional swings reflected in candles. In less liquid or ‘meme’ stocks, coordinated retail buying/selling based partly on simple technicals (including candles) can create temporary self-fulfilling effects. Patterns become useful as signals of specific crowd behaviour rather than pure price predictors. The combination of a pattern with social media sentiment or unusual volume offers clues. Effectiveness be higher on longer timeframes (daily/weekly) than intraday, where HFT noise is overwhelming.
Key Event Influence: COVID-19 Crash & Rally; Meme Stock phenomenon (GME, AMC); rise of commission-free brokers; increased social media influence (Reddit/WSB); growing use of AI in trading; geo-political uncertainty; and inflation/rate hike cycle.
Our core metric is straightforward: Success Rate. We defined success as the price moving at least 1x the Average True Range (ATR) in the pattern's predicted direction within the next 3 bars. See Methodology for full details.
Data
Major Stock Indices: S&P 500, Nasdaq, Dow, FTSE, DAX, Nikkei
Major Large Cap Stocks: A representative basket of 30 globally significant, liquid stocks with long histories. Major Futures: ES, NQ, CL, GC, ZB, 6E
Major FX Pairs: EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD
127 million BARS ANALYSED
4,240,000 PATTERN ENCOUNTERS
Our Findings
Success Rate (1984 - 1995)
Bullish Patterns
Bearish Patterns Average
Highest success rates due to novelty and less efficient markets. Strong reversal and confirmation patterns have higher rates.
1: Average Success rates (era 1984-1995)
1996 – 2007
General decrease across the board (e.g., -3% to -7%) as patterns became common knowledge and early algos emerged. Simple, popular patterns (Engulfing, Hammer) see slightly larger drops than complex ones initially.
Chart 2: Average Success Rates (Era 2: 1996–2007)
Chart
2008 – 2019
Significant drop (-5% to -9% from Era 2) reflecting algo dominance and market efficiency. Most patterns hover just above the 50% noise level, suggesting minimal standalone edge. Stronger multi-candle patterns retain a slightly higher (but still low) hypothetical edge.
2020 – 2024
A complex picture:
• Slight increase (+2% to +3% from Era 3) for many patterns, especially strong reversal types (Stars, Engulfing, Hammer/ Shooting Star), reflecting potential for clearer signals during high volatility and retail-driven emotional swings.
• Indecision patterns remain weak standalone.
• Continuation patterns see minimal improvement, possibly struggling in volatile, choppy conditions unless a very strong narrative trend takes hold.
• Overall effectiveness remains far below historical peaks due to algorithmic sophistication but slightly better in specific volatile situations than the hyper-efficient Era 3 baseline. Context is absolutely paramount here.
Chart 3: Average Success Rates (Era 3: 2008–2019)
Chart 4: Average Success Rates (Era 4: 2020–2024)
Important Caveats
Standalone v Context: These numbers consider the pattern somewhat in isolation. A real trader uses patterns in conjunction with trend analysis, support/resistance, volume, indicators, etc. Context significantly impacts actual success.
Timeframe: Although most bars were generated on 5-minute charts, we placed greater emphasis on daily charts, as intraday pattern effectiveness decayed more rapidly and significantly due to high-frequency trading noise.
Liquidity & Asset Class: Effectiveness varied greatly by market (stocks, forex, futures) and liquidity. These are broad averages, most applicable to liquid futures markets and large-cap stocks.
Success Rates by Pattern Type
Chart 5: Bullish Reversal Patterns and Table
Chart 6: The era box
Chart 8: Another Era Box
Chart 7: Bearish Reversal Patterns and Table
9: Bearish Confirmation Patterns and Table
Chart
Chart 11: Bearish Confirmation Patterns
Chart 10: Era box for Bullish Marubozu
Chart 12: Another Era pattern box
Key Takeaways
• Short-Term Churn v Longer Signals: We noticed intraday patterns (5-min, Hourly) generally showed more dramatic shifts (both positive and negative) compared to Daily patterns. This might reflect the increased impact of algorithms on shorter timeframes.
• The Golden Age: In the early 80s, some top candlestick patterns boasted 70% success rate – a level never seen since.
• The Great Decay: On average, popular candlestick patterns lost 10-15% of their predictive success between their 1980s peak and the late 2010s.
• From Boom to Bust: Bullish pattern effectiveness soared to nearly +10% above average in the '99 Dot-com peak, only to see bearish patterns gain a similar edge in the 2000-2002 crash.
• The 2010s Quiet Zone: For nearly a decade, most candlestick patterns flatlined, offering minimal predictive edge in an algo-dominated, QE-infused market.
• The Retail Resurgence: The COVID-19 pandemic and retail trading boom led to a resurgence in candlestick pattern effectiveness, particularly in volatile markets. Patterns like the Hammer and Engulfing showed notable improvements, suggesting that retail-driven volatility can create clearer signals.
13
Back to the Game: Perhaps the most important takeaway - candlestick patterns have returned to a positive expentancy zone after spending a decade near the noise threshold.
Chart
Our Methodology
Here’s how we approached this analysis:
• Data Sources: We utilized high-quality End-of-Day (EOD) and Intraday (1-Hour, 5-Minute) data for a diverse basket of liquid assets, including major stock indices (S&P 500 components), Forex pairs, and key commodities, spanning from 1984 to late 2024. Sourcing varied by asset class and timeframe.
• Pattern Recognition: We employed standardized, widely accepted definitions for 20 common candlestick patterns. Pattern recognition logic was applied consistently across all data. Specific definitions based on common technical analysis literature were used.
- Technical Analysis Explained by Martin J. Pring (3rd edition, 1991)
- Trading for a Living: Psychology, Trading Tactics, Money Management by Dr. Alexander Elder (1993)
- Beyond Candlesticks: New Japanese Charting Techniques Revealed by Steve Nison (1994)
- Technical Analysis of the Financial Markets by John J. Murphy (1999)
• Volume Analysed: The analysis covered over 4,000,000 identified pattern instances across all assets and timeframes.
• Era Definitions: Era 1: Jan 1984 - Dec 1995. Era 2: Jan 1996 - Dec 2007. Era 3: Jan 2008 - Dec 2019. Era 4: Jan 2020Dec 2024.
• Success Criteria: A pattern instance was deemed 'successful' if the price moved >= 1x the 14-period Average True Range (ATR) in the pattern's expected direction within the 3 bars immediately following the pattern's completion. The ATR was calculated based on the period leading up to the pattern.
• Tools: Analysis performed using Python (Pandas, NumPy), custom pattern recognition scripts, as well as NinjaTrader's NinjaScript and TradeStation's EasyLanguage.
Patricia Elbaz puts questions to Michael Hewson, award winning Technical Analyst, Trader and Market Commentator.
PATRICIA ELBAZ, MSTA Director
Michael it’s a pleasure to interview you for the Market Technician Journal, especially as I, along with thousands, have followed your market commentaries for many years at CMC Markets.
First of all, Congratulations and welcome to the STA Board! Tell us, how were you first introduced to Technical Analysis?
MH: It was a long time ago when I started working at Commonwealth Bank of Australia in 1989 that I was introduced to TA. We had the use of a Teletrac service which was used by the guys in Sydney to send us reports on the day-to-day movements in various currency pairs. This piqued my interest and I was interested to learn more about the topic and it went from there.
“On the whole many clients tend to use their gut instinct when trading particular markets, perhaps looking at the fundamentals but generally herding around a common consensus which usually tends to be incorrect.”
MICHAEL HEWSON
Which indicators would you recommend using when starting to learn about Technical Analysis?
MH: Before even starting to look at which indicators to use when starting to learn about TA, it’s important to understand the concept of trend when looking at a market, as well as being able to tell the difference between a trend following indicator, and a range following indicator.
A simple moving average is usually a decent benchmark for an overall trend but is only helpful for those traders looking to trade in the direction of the underlying trend.
MICHAEL HEWSON
Other basic indicators are the Relative Strength Indicator (RSI) and the Stochastic but these are next to useless in a strongly trending market unless you are looking to time your entry into a trade in the direction of the underlying trend.
I would start with a simple moving average along with an RSI or Stochastic as a starting point but one should always remember the trend and the price should always take precedence over oscillators or other arithmetical indicators.
In your experience at CMC markets, were clients using both fundamental and technical analysis when making trading decisions?
MH: On the whole many clients tend to use their gut instinct when trading particular markets, perhaps looking at the fundamentals but generally herding around a common consensus which usually tends to be incorrect. When I spoke to them, I always challenged them with their reasons for being in a trade at all and only very few were able to answer that question satisfactorily. Those who were able to answer that question were unsurprisingly the most successful ones.
In the past, many traders have said that they find technical analysis very useful for short term trading and outlook. Would you agree with that, or do you think that a long-term time frame is just as significant?
MH: It depends on your time horizon but longer-term analysis can be just as important as short-term scalping trades. A long-term support and resistance line can be a formidable barrier to further gains and losses in the context of a strong move up or down and used to take some profit on a particular trade. It can also be used to identify a significant breakout level and change of trend.
Finally, do you see the development in AI and algo trading as a positive move in the future?
MH: This is a tough one: used in the right way, any new advancement in technology can be used to improve the decision-making process, as well as being able to perform research more quickly on a range of different markets.
On the minus side, as I have found out by being AI deepfaked, it can be used for more nefarious purposes to try and swindle people out of their hard-earned cash.
Used in the right manner, it can be a force for good but there need to be strong guard rails in place to ensure it is not abused. As far as the latter point is concerned, we remain quite a way short of that.
Thank you so much Michael for sharing with us and the many readers your knowledge and wide experience in trading and education.
We hope to see you at future STA events
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Renko Chart Trading Revolution
Overview
In Renko Chart Trading Revolution, author and quant strategist Stephen Hoad presents a bold and highly detailed exploration of Renko charts — one that goes far beyond conventional introductions. This 433page publication positions Renko not as a side technique but as the core of a complete, probability-based trading framework that incorporates quantitative edge, trading psychology, and automation. It is a book with significant ambition and for the most part it delivers,offering traders a highly structured, multi-dimensional path to strategy development using Renko charts.
Structure and Scope
The book is intelligently structured, beginning with the essential foundations of Renko chart construction - including traditional and hybrid approaches, volatility filtering, and time-conditioning - before progressing to more advanced trading applications. For readers unfamiliar with Renko or those wishing to revisit the chart type with fresh eyes, these early chapters are particularly strong. Hoad’s attention to how Renko charts filter out market noise and present “price truth” without timebased distractions is clearly rooted in practical experience. His explanations of block size selection, confirmation signals, and breakout structures demonstrate both depth and clarity. One of the more unique features of the book is its integration of mindset and trading psychology through Hoad’s “Samurai Trader” concept. Drawing from Musashi’s Book of Five Rings, this section offers philosophical but actionable ideas on discipline, detachment, and focus — ideas that align well with the demands of highperformance trading.
Strategy and System Development
Where Renko Chart Trading Revolution truly excels is in its middle and later chapters. Hoad introduces three well-documented trading strategies -
trend following, mean reversion, and breakout momentum - each designed around Renko chart principles. These strategies are not offered in isolation, but as part of a broader discussion on:
• Edge development
• Risk control
• Expectancy modelling
• Trade management
These chapters reflect Hoad’s deep background in institutional trading and quantitative thinking. He brings concepts such as the Kelly Criterion, edge theory, and game theory into the discussion without ever overwhelming the reader. The balance between theory and practical application is handled thoughtfully.
Automation and Coding
The final part of the book addresses automation and system development in Python, providing a roadmap for traders wishing to move toward algorithmic execution. While not a substitute for hands-on coding practice, this section gives clear structural guidance and encourages readers to think modularly when developing Renko systems.
Traders with some programming background will appreciate this section most but even discretionary traders will benefit from the way Hoad explains system logic and process building.
Style and Presentation
The writing is professional and accessible, with clear language, logical flow, and effective use of diagrams, examples, and summaries. Hoad manages to keep a strong narrative voice throughout, grounding abstract ideas in the day-to-day reality of the trader.
At times, the book’s ambitioncovering philosophy, psychology, strategy, and code - does stretch its cohesion. Some readers might wish for deeper treatment in isolated areas, such as behavioural models or coding walkthroughs but the book remains focused on its central goal: delivering a unified framework around Renko charts and trading edge.
Contribution to the Field
This is a notable addition to the literature of modern technical analysis. Renko charts have often been underexplored in Western professional contexts, and Renko Chart Trading Revolution does an excellent job of addressing this gap.
Its true contribution lies in elevating Renko from a niche tool to a central component of a rulebased, probability-informed trading methodology. For analysts and traders seeking to systemise their visual process - or explore alternatives to time-based charting - this book offers both insight and utility.
FINAL VERDICT
Stephen Hoad’s Renko Chart Trading Revolution is one of the most comprehensive works on Renko charting I have seen. It succeeds in combining technical precision, strategic depth, and philosophical insight in a single volume. While its scope is broad, the execution remains grounded and practical throughout.
It is an essential read for technical analysts interested in Renko, system development, and the blending of discretionary and algorithmic thinking.
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STA Calendar 2025/26
STA Monthly Meeting (September 2025)
SEP 9
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STA Monthly Meeting (February 2026)
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Description
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STA Diploma Part 1 Course
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Booking is well underway for the CISI and IFTA accredited online STA Diploma Part 1 and Diploma Part 2 courses. The two courses have been designed to cater for newcomers and experienced professionals who are looking to challenge themselves. They will learn to develop the methodology, tools and confidence to make better informed trading and investment decisions in any asset class, anywhere in the world.
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