Trading for a living

Yeah RN Elliott was onto something, but he just didn't go far enough, if he had, he'd of found a better fractal sequence on all time-frames and market movements

Okay. Yes, I'm a bit confused by all the things that have already been shared here as "strategies." In an earlier post, you wrote: "Yes, you need only one approach, which is what I have provided you. Something backtested and solidly profitable."

So, not sure which strategy the last sentence referred to. It sounded to me like a fully functional and profitable strategy that could achieve my goals. Presumably you meant the ORB strategy from your earlier TradingView link provided instead of the GBP/USD M30 Renko Chart idea by AI?

Two more questions.

1.) What type of trading strategy would you classify this Renko GBP/USD M30 issue under? I assume it's a trend-following approach?

You wrote: "Short story: 10 KUSD, risk 1% or 100 USD with a 15 point stop. Hit-rate should be a least 65% with professional execution and Profit Factor 2.14-3.19.

This also irritated me a bit, as most trend-following strategies tend to have a rather low win rate and probably wouldn't be particularly suitable for short-term trading, where you could achieve a consistent minimum return over intervals of one to three months, especially if you only want to trade the same market every day and for a limited time available.

Also from AI:

1. "A typical trend following strategy has a win rate of about 30-50%, but this can be less important than a good profit factor, which is often targeted between 1.5 and 2.0"

Which brings me to my second question.

2.) What characteristics and key performance metrics should a promising strategy have in your backtest (and over what period or number of sample trades)
before you would use it in live trading?

Here are some of the frequently mentioned metrics to evaluate trading strategies and some tables with recommended values

1. Profit Factor: Tracks profitability by comparing total profits to total losses.
2. Maximum Drawdown (MDD): Measures the largest drop in account value to assess risk.
3. Sharpe Ratio: Evaluates risk-adjusted returns to balance risk and reward.
4. Win Rate: Tracks the percentage of successful trades.
5. Expectancy: Calculates the average profit or loss per trade over time.
   
   
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It is, but I also said that you would have to do some independent thinking in order to verify it, likely improve it, and work out exact setups, entry/exit criteria etc.


It's not a trend following approach. It's a swing trading approach that trades the smallest and safest swings in the structure of this market. It should have a hit rate of 65-80%+, a reward:risk of at least two, and a Profit Factor of at least 2.5.


As stated above. The longest backtest ever done was over 6 months of history, done by a previous trading partner.

And your model keeps on performing (and I've got another preliminary entry trigger for you: the WMA5 HiLo channel). Another 30+ pips for 15 pips risk in the morning, and even more in the afternoon. See chart below.

PS: If you plan to trade the morning sessions only, you should get in front of the computer before 8am CET - that's when things get started in London. Do some backtesting and you'll see many nice setups around that time.

Yes, and I had Perplexity AI put together the following for you:

Sacred Geometry vs. Multifractal Geometry in Financial Markets

The question touches on a fundamental distinction between two approaches to understanding patterns in financial markets: sacred geometry and multifractal geometry. While both claim to reveal underlying order in market movements, they differ dramatically in their mathematical foundations, empirical validity, and philosophical underpinnings.

Sacred Geometry in Trading
Sacred geometry in financial markets refers to the application of classical geometric patterns and ratios believed to have mystical or universal significance. This approach primarily relies on:

Fibonacci Retracements and Extensions: Based on the golden ratio (1.618) and related ratios like 0.618, 0.382, and 0.236, these levels are used to identify potential support and resistance zones. The "golden zone" between 50% and 61.8% retracement levels is particularly emphasized.

Harmonic Patterns: These include complex formations like the Gartley, Butterfly, Bat, and Crab patterns, which combine specific Fibonacci ratios to predict potential reversal points. Each pattern follows precise geometric rules and ratio requirements.

Gann Theory: W.D. Gann's approach incorporates geometric angles, time cycles, and mathematical relationships, treating markets as following natural geometric laws.

Classical Chart Patterns: Traditional formations like triangles, head and shoulders, and ABCD patterns that appear to follow geometric principles.

The appeal of sacred geometry lies in its self-fulfilling prophecy effect—when many traders react to these levels simultaneously, they can create actual turning points in markets. However, this is fundamentally a psychological and behavioral phenomenon rather than evidence of inherent geometric laws in markets.

Multifractal Geometry: The Mathematical Reality

Multifractal analysis represents a rigorous mathematical approach pioneered by Benoit Mandelbrot, who applied fractal geometry to financial markets. This framework reveals that financial markets exhibit genuine mathematical properties:

Self-Similarity Across Scales: Market movements display similar statistical patterns whether viewed on minute, daily, or monthly timeframes. This isn't mystical—it's a measurable mathematical property where patterns repeat at different scales.

Multifractal Spectrum: Unlike sacred geometry's fixed ratios, multifractal analysis reveals a spectrum of scaling exponents that describe how different aspects of market volatility behave across time scales. The multifractal spectrum f(α) provides a complete statistical description of the market's complexity.

Long-Range Dependence: Markets exhibit statistical memory where past large changes influence future volatility patterns—a phenomenon called volatility clustering. This creates genuine mathematical persistence that can be quantified using the Hurst exponent.

Heavy-Tailed Distributions: Real market movements show "fat tails"—extreme events occur far more frequently than predicted by normal distributions. This is a statistical reality, not a geometric interpretation.

Power Laws: Market movements follow power law relationships, meaning the frequency of large price movements scales according to mathematical laws rather than geometric ratios.

The Critical Differences

Mathematical Rigor: Sacred geometry relies on pre-selected ratios and patterns often chosen for their aesthetic or mystical appeal. Multifractal analysis uses statistical methods to empirically measure actual scaling properties in market data.

Predictive Power: Sacred geometry patterns work primarily through trader psychology and self-fulfilling prophecies. Multifractal properties reflect genuine statistical regularities that can be measured and potentially exploited.

Empirical Validation: Studies consistently show that markets violate the assumptions of simple geometric models—they don't follow strict self-similarity as sacred geometry suggests. Instead, they exhibit multifractal characteristics with complex, measurable scaling properties.

Time Horizon Dependency: Sacred geometry typically focuses on specific price levels and static patterns. Multifractal analysis reveals how market behavior changes across different time scales, providing a dynamic understanding of market structure.

Risk Understanding: Sacred geometry can lead to false confidence in geometric "support" levels. Multifractal analysis reveals the true nature of market volatility clustering and extreme events, providing more realistic risk assessment.


Mandelbrot's Revolutionary Insight


Mandelbrot fundamentally challenged the smooth, predictable models of traditional finance by showing that markets exhibit "wild randomness" rather than "mild randomness". His work revealed that:

1. Market prices follow fractal rather than smooth geometric paths
2. Extreme events cluster in time through multiplicative cascade processes
3. Traditional risk models dramatically underestimate the probability of large losses
4. Markets have memory effects that persist across multiple time scales

This isn't about sacred ratios—it's about the mathematical structure of complex adaptive systems.

Practical Implications

While sacred geometry may provide useful psychological anchors for trading decisions and can work through self-fulfilling prophecies, multifractal analysis offers:
Superior Risk Management: Understanding true volatility clustering and heavy-tailed distributions leads to more robust position sizing and risk controls.
Better Market Timing: Recognizing multifractal patterns across time scales can improve entry and exit timing beyond simple geometric levels.
Realistic Expectations: Multifractal analysis reveals that markets are inherently more volatile and unpredictable than sacred geometry suggests.
Advanced Modeling: Modern quantitative funds increasingly use multifractal models for portfolio optimization and risk assessment.

Conclusion

The evidence overwhelmingly supports multifractal geometry as the more accurate framework for understanding financial markets. While sacred geometry patterns may provide useful trading tools through behavioral mechanisms, they don't represent fundamental mathematical laws governing market behavior.
Mandelbrot's fractal approach reveals that markets are complex adaptive systems with genuine statistical regularities that can be measured and understood mathematically. This isn't about mystical ratios—it's about recognizing that financial markets exhibit the same type of scaling properties found throughout nature, from river networks to weather patterns.
For serious market analysis, multifractal geometry provides a rigorous foundation for understanding market dynamics, while sacred geometry remains primarily a tool for trader psychology and behavioral pattern recognition. The future of quantitative finance lies in embracing the mathematical reality of multifractal markets rather than imposing geometric ideals that markets don't actually follow.

Here are my current results for the ORB system I'm testing.

I think some of the performance metrics aren't that impressive (i.e. Sharp Ratio), but the account is still relatively new, and the system probably needs further improvement, especially to achieve my stated goals (min. 2-3%/month). Currently, I'm at a return of +7.0% with a maximum drawdown of -5.3%. My minimum target of a 2% return per month, which I need, would be covered for September.

Without going into the full details of the strategy logic. It trades range breakouts within the first 5-40 minutes after the opening of the respective market.

These are currently GER30, NAS100, and XAUUSD.

Orders are opened upon a direct breakout of the high/low (no waiting for candle closes) of the corresponding ranges in both directions (long/short) until the end of the day (1 hour before the underlying market closes). No "trend" filter is used to skip orders by attempting to determine a kind of pre-detection of the prevailing trend direction. It only checks for a minimum/maximum breakout range in advance, based on a dynamic calculation of the ADR 10.

Risk Settings: 0.25% risk per balance per order and a maximum of $100 per order/day/symbol based on a $10,000.00 account.
Stops & Targets: Based on a multiplier of the ADR with partial take profits.

Results (As of October 17, 2025) | ORB-2 (GER30, NAS100, XAUUSD)

Account Opened: Sep 21, 2025
Account Age: 26 days
Base Currency: USD
Max Leverage: 100:1

Starting Balance: $10,000.00
Current Balance: $10,701.27
Total P&L: +$701.27
Total Trades: 99
Commission: -$7.91

Win Rate: 41.4%
Profit Factor: 1.33
Consecutive Wins: 6
Consecutive Losses: 8
Avg Consecutive Wins: 2.0
Avg Consecutive Losses: 3.1
Average Profit: +$69.50
Average Loss: -$37.04
Risk Reward Ratio: 1.88
Sharp Ratio: 0.11
Max. Drawdown: -5.30% (-$568.28)
Absolute Drawdown: 0.00% ($0.00)
Max. Daily Profit: 5.27% (+$535.81)
Max. Daily Loss: -3.77% (-$398.16)
Best Trade: +$256.77
Worst Trade: -$67.87
Average Trade Size: 0.55 Lots
Average Hold Time: 2.1h

Symbol Performance Analysis:

XAUUSD

Trades: 28
Total P&L: +$672.59
Win Rate: 53.57%
Avg Profit: $78.42
Avg Loss: $38.74
Profit Factor: 2.34

GER30

Trades: 35
Total P&L: +$38.95
Win Rate: 34.29%
Avg Profit: $67.08
Avg Loss: $33.3
Profit Factor: 1.05

NAS100

Trades: 36
Total P&L: $-2.36
Win Rate: 38.89%
Avg Profit: $62.31
Avg Loss: $39.76
Profit Factor: 1.0

Thank you medici for such a detailed response. I was just curious to ask my question because in the world of economic markets (all markets not just FX) I have very commonly heard this phrase that "My strategy worked in previously but now doesn't". The most common cause to this statement is that the "edge" from that strategy is no longer. But your situation is different because you said it worked some years ago, stopped working, and now again your using the same strategy. But your explanation tells me the reason was not that the "edge" disappeared, it was your own discretion that the risk-to-reward ratio was not meeting your demands. So yes, you have been helpful.

Your second response was actually really insightful. I didn't knew that people in forex factory actually care about academic papers/journals. It's a first for me. And I am really happy that you are actually paying attention where not most people look. I have always been fascinated by cross-field work. Meaning, work in physics can be applied to trading, or even astronomy. The field I found the most interesting was 'Econophysics'. This merges Economics and Physics.

I have read many books regarding econophysics and most of my work (strategy) has only been inspired from this field, not directly used. This was indeed an interesting conversation to have. Good luck to you.

You lot like your AI don't you

I've been applying the 2 since 2013

I mentioned in this thread that we're forced to view price action on a 2D screen and format - if we could see in higher dimensions which we will never be able to do, then we'd see all the geometry and fractals

You can still get there with Elliott Wave type counts, but the growth/decay isn't linear due to the 2D - But you can count x swings up, x swings down and then it repeats and it does the so on every time-frame - but you HAVE to get the size of the swing right, unless it doesn't work and it doesn't follow RN Elliotts 5 up 3 down, he was close, but not quite enough

The reason we see sacred geometrical levels is because the markets are building geometric structures - again we cannot see them due to viewing in 2D, but they are there and you only get to see the geometry at nodal points where it fits EXACTLY and by EXACTLY I mean within 2 points of perfection

I disagree of the self-fulfilling prophecies bit, AI's got that wrong - last chart in this sequence, I bet only a tiny handful of traders around the world were aware of it - everyone looks at key levels on retracements and they still work - if the market is building a geometric structure as the SP500 is, its impossible for it to skip key geometric levels in that structure - people just don't understand what the planets are doing and how the markets are working to their movements

Cannon of Proportion published by Bryce Gilmour back in the late 90/s early 2000's - SACRED GEOMETRY

Gann is the real superstar here though, it's what his squares were built for in the early 1900's


This is as it says on the chart - multiple geometric ratio levels spun out from high to lows of significance - it wasn't hard to see 2022 was significant - It was possible to nail the high to within 1 point - I doubt many people knew about it though

All I did here was take the HIGH to LOW RANGE and project the GEOMETRIC LEVELS greater than 100% UPWARDS to show you that markets react to levels - to NAIL HIGH's you need to do a few more things, but as you can see just from this one technique the 2022 HIGH showed a mass clustering of geometric levels that you had to be alerted to and it turned out to be a decent bear market thereafter!



Just to "prove" to any doubters

I was out on the time by 2 weeks, but price stopped right in that RED BOX

I just didn't show the geometric levels



2 Fridays ago the market stopped dead on another key geometric level




2.236 is the square root of 5, Diagonal of 2 adjacent squares

To get the ratio you simply take the RANGE of the 2009 LOW to 2020 HIGH and multiply by 2.236

This doesn't mean a mega crash, it depends on where in the geometric structure we are and whether that 2.236 level is a major or minor part of the build, if its minor, the correction will be small, if its major, then the correction will be significant

Both sacred geometry and multi-fractal work together in unison - we just get to see differing parts of it at various times due to the 2D

For Sacred Geometry to work, you have to have multi-fractual geometry at play all the way through

PS - the EX RETS example is just that - ALL valid ratio's on the chart to show the clustering of geometric levels - SEPERATE calculations were required to get within 1 point of the high [not shown]

For anyone reading this and thinking "I can't do that or learn about it" - YOU DON'T NEED TO, YOU CAN TRADE WITHOUT KNOWING ANYTHING ABOUT GEOMETRY

THTpro, you absolutely correct

Was that what I said? I suppose it's a matter of etymology.

I did mean to say that the edge DID disappear. Still, the market retained its structure, it's just that it was NOT tradeable anymore.

Why? Because the volatility was gone, which I would explain by the simple fact that volatility is cyclic. In a market there are periods of greed, periods of fear, periods of uncertainty, and periods of equilibrium. Each have its own characteristics, and generally periods of equilibrium don't offer many tradeable edges.

It seems like Brexit, and the general turmoil in both the UK and the US, have contributed to GBPUSD being quite tradeable. And most recently the political turbulence in Japan has made yen futures attractive. That's uncertainty. Greed has gripped the gold market, which is now the place to be for ambitious speculators. Etc, etc.




Thank you. I do remember the boom in econophysics papers 15-20 year ago. Would you mind sharing a bit on how that field inspires you?

No, I don't like Ai in general, but I love Perplexity Ai.

And the sentiment expressed in the post is not necessarily mine. I have to admit that I posted it partly to stimulate debate

You may very well have a point, but you reason based on an example or two. It's like saying "all swans are white" when you've only seen two swans in your whole life.

But don't worry. I've engaged Perplexity AI, and somebody whose work and writings i admire and respect, on your side. How do you like that, then?


Tony Plummer on Market Cycles and Market Psychology

Tony Plummer is a distinguished technical analyst and former fund manager who has profoundly influenced the field of market psychology and cycle theory. His work bridges behavioral finance, technical analysis, and natural systems theory to explain how financial markets behave through predictable patterns rooted in crowd psychology.

Core Philosophy: The Crowd and Independent Decision-Making

Plummer's central thesis emphasizes that the ability to make money in financial markets depends critically on an individual's capacity to make decisions independently of the crowd. His seminal work, Forecasting Financial Markets: The Psychology of Successful Investing, first published in 1989 and now in its sixth edition, demonstrates how the herd instinct in decision-making leads to disastrous results for most market participants.

To achieve trading independence, Plummer identifies three essential interrelated skills:

Understanding forces in logical terms: Investors must develop a philosophical framework that genuinely comprehends the dynamics driving market fluctuations. Markets oscillate regularly and, according to traditional theory, unpredictably—recognizing this reality prepares traders for the emotional challenges ahead.

Recognizing and neutralizing emotional responses: Plummer emphasizes "emotional intelligence" in trading, requiring investors to understand their vulnerability to market influences and adopt responses appropriate to market trends rather than hostile to them. This dimension addresses the psychological biases identified in prospect theory, including the endowment effect, loss aversion, and reference point dependency.

Designing objective trading systems: Developing an investment process or trading system that generates objective buy and sell signals removes the subjective decision-making that leads investors to cut profits early and run losses—behaviors driven by fear and anxiety.

The Bull-Bear Life Cycle and Limit Cycle Theory

Plummer's analysis reveals that financial market crowds follow a limit cycle pattern between prices and sentiment. Each crowd—bullish or bearish—has its own life cycle, and during market reversals, the two crowds may coexist until one dominates once a trend establishes itself.

The bull-bear cycle exhibits a characteristic three-wave pattern that mirrors natural processes of learning and energy absorption in living organisms. This archetypal pattern exists not only in financial markets but also in economic activity, revealing an organized and essentially predictable world beneath apparent chaos. Plummer describes this recurring pattern as the "price pulse" of the market, providing a workable model for how traders react to new, market-moving information.

The crowd psychology evolves through distinct emotional phases. At market peaks, the bullish crowd experiences maximum optimism and belonging, while at troughs, fear and isolation dominate. The shift from isolation to belonging creates the limit cycle between prices and behavior, with beliefs and leadership playing crucial roles in crowd formation.

The Golden Ratio and Mathematical Foundations

Central to Plummer's analytical framework is the golden ratio (phi = 1.618 and its inverse 0.618), which he identifies as a fundamental organizing principle in market behavior. This ratio, expressed in the Fibonacci sequence, provides mathematically derived numbers that—together with the price pulse—enable traders to forecast major tops and bottoms with remarkable precision.

Plummer demonstrates the golden ratio's predictive power through historical examples. Between 1929 and 1932, the Dow Jones average fell 344 points; according to the golden ratio, this established an upside target of 1000, which was hit in 1965, marking a 17-year turning point in the market. The golden ratio appears in retracement levels (61.8%, 38.2%, 23.6%) and extension levels (161.8%, 261.8%) widely used in technical analysis.

The golden spiral, representing the adjustment process following an information shock, can be viewed in three dimensions: the rate of change in prices, the level of sentiment, and time. This spiral relationship between price changes and sentiment creates the characteristic wave patterns observable in market movements.

The Law of Vibration and William Gann

In his 2013 book The Law of Vibration, Plummer presents groundbreaking research into the work of legendary trader William D. Gann. Plummer argues that the Law embodies a very specific pattern of oscillation that accompanies change and evolution, found in both financial market fluctuations and economic activity.

Through 20 years of investigation involving gematria, the enneagram, and financial market analysis, Plummer reveals that this pattern was known in antiquity and buried in St. Matthew's Gospel in the Bible. It was also concealed in the early 20th century in the structure of books by Gann and mindfulness exponent George Gurdjieff, both preserving this knowledge in hidden form.

Gann's "mass pressure pattern" shows how momentum and absolute price levels interact, with shocks creating information gaps that drive market oscillations. Plummer's work demonstrates that Gann's references to the "law of three" (positive, negative, and neutral forces) directly correspond to the golden ratio: 0.618 as the receptive part, 0.382 as the generative part, and unity as the neutral reconciliation.

Economic Cycles: The Hierarchical Framework

Plummer integrates multiple time-scale cycles into a comprehensive hierarchical framework. His system recognizes several major economic cycles:

Kitchin cycles (inventory adjustment cycles): Average 3.33 years, related to inventory fluctuations and closely aligned with the US presidential cycle. The Foundation for the Study of Cycles documents this as a 41-month rhythm predicting major equity market peaks.

Juglar cycles (capital investment cycles): Last 7-11 years, averaging around 10 years, and represent three times the Kitchin cycle duration. These cycles exhibit distinct phases: transition (eliminating redundant beliefs), transformation (laying down new infrastructure, ending with excesses but not crashes), and termination (speculative rather than innovative, akin to Elliott Wave 5).

Berry cycles (infrastructure cycles): Last 25-30 years, averaging 30 years, and represent three times the Juglar cycle. Berry cycles have three distinct forms: terminal cycles (characterized by fear, resistance, and accusation), adaptation cycles, and regeneration cycles.

Kondratyev waves (price cycles): Approximately 54 years, representing long-wave price oscillations. Importantly, Plummer notes that not all Kondratyev lows coincide with major depressions because they are price cycles rather than pure economic cycles.

Strauss-Howe metacycles (crisis cycles): 90-99 years, representing generational turnings that alternate between deflation and accelerating inflation. Plummer believes we finished the deflation metacycle in 1946 and are now in an inflationary cycle expected to push until around 2030, potentially marked by major global trauma.

These cycles build upon each other through power law behavior, with each level being approximately three times the duration of the lower-order cycle. This fractal-like structure means cycles nest within each other, creating a comprehensive timing framework.

The Life Cycle Hypothesis

Plummer's 2018 book The Life Cycle Hypothesis provides evidence of an ordered process behind the apparent randomness of financial asset price movements, economic fluctuations, and social trends. The hypothesis demonstrates that genuine information shocks create dramatic effects on any system into which they are inserted, generating reactions that are essentially pre-programmed.

These reactions involve processes of advance and decline, embracing specific lower-order fluctuations. The shock of fresh information creates a new organism whose energy travels along a natural pathway between birth and death. This pathway generates diverse phenomena including personal mid-life crises, the swarming of innovations, recurring patterns in financial markets, and rhythmic oscillations in national economies.

Plummer identifies this pathway as producing the Great Depression of the 1930s, the inflation trauma of the 1970s, and the global financial crisis of 2007-08. The same pathway suggests a major global crisis in the early years of the next decade (around 2024-2030).

Neuroscience, Psychology, and Trading Behavior

Plummer's background includes four years of training in Core Process Psychotherapy and qualification as a Neuro-Linguistic Programming (NLP) practitioner, informing his understanding of trader psychology. His research emphasizes how the brain's limbic system and neocortex influence trading decisions.

Key psychological factors include:

Prospect theory biases: The endowment effect (ownership increases value), loss aversion (losses have greater psychological impact than gains), and reference point dependency (earlier valuations influence current decisions) explain why traders cut profits early and run losses.

Fear and anxiety responses: The brain cannot distinguish between imagined threats and actual threats, meaning fear and anxiety about market positions trigger the same physiological responses as real danger. This leads to the sympathetic nervous system mobilizing the fight-or-flight response, often causing traders to act impulsively with the crowd rather than following objective rules.

The winner effect: Rising testosterone levels during profitable trading create a feedback loop with dopamine, producing pleasure and anticipation that can lead to overconfidence and excessive risk-taking. Research shows this effect is ten times stronger in men than women, suggesting women may be less prone to this bias.

Volume and sentiment: Market bottoms typically show high volume as panic selling dominates, while market tops exhibit prolonged rollover periods with gradually increasing volume, consistent with prospect theory's predictions about loss aversion driving faster selling than profit-taking.

Practical Application: Trading Systems and Technical Analysis

Plummer's work provides practical frameworks for technical analysis. He emphasizes:

Elliott Wave integration: While Plummer references Elliott Wave Theory, his three-wave pattern offers a more flexible framework applicable across multiple time scales. The five-wave impulse pattern and three-wave corrective pattern align with his natural oscillation principles.

Momentum and oscillators: Technical indicators measuring momentum—including RSI, stochastic oscillators, MACD, and rate of change—provide objective signals for identifying overbought and oversold conditions. These tools help traders recognize when crowd sentiment reaches extremes.

Fibonacci retracements and extensions: The 23.6%, 38.2%, 50%, 61.8% retracement levels and 161.8%, 261.8% extension levels derived from the golden ratio provide specific price targets for forecasting turning points.

Non-confirmation and divergence: Comparing price action with momentum indicators reveals when trends are losing strength, providing early warning signals of reversals.

Volume and open interest analysis: These metrics measure crowd participation intensity, with high volume at lows indicating capitulation and building volume at highs suggesting distribution.

Current Market Context and Future Outlook

Based on Plummer's cycle framework, we are currently in the post-1980 Berry regeneration cycle, within a Juglar disruption cycle that began around 2008. The current positioning suggests:

The 54-year Kondratyev cycle in wholesale prices from 2002 points toward 2057, with critical transition points around 2024-2027. Historical patterns of rapidly rising money supply combined with war suggest heightened risk in this timeframe.

The Berry crisis cycle alignment with the Strauss-Howe metacycle indicates a major terminal phase approaching around 2030, consistent with Plummer's forecast of potential economic trauma.

The Kitchin cycle (41-month rhythm) predicted a major equity market peak in Q1 2025, with further asymmetric risk between 2026-2027 according to the Foundation for the Study of Cycles timing model.

The current multi-decade cycle is inflationary with rising interest rates, favoring energy, staples, materials, and utilities sectors. This contrasts with the deflationary environment of earlier decades.

Legacy and Influence

Tony Plummer's contributions extend beyond his published works. As a Fellow of the Society of Technical Analysts in the UK and former Visiting Professorial Fellow in Economics at Queen Mary, University of London, his influence shaped technical analysis education. His book Forecasting Financial Markets remains essential reading for the Society of Technical Analysts diploma learning path.

His professional career from 1976 to 1999 as a fund manager demonstrated practical application of his theories, with retail investment funds under his management achieving long-term first quartile performances and numerous industry awards. Now retired from active trading, he continues to specialize in strategic economic and financial market analysis.

Plummer's work provides a comprehensive framework uniting psychology, natural patterns, mathematical relationships, and cycle theory. By revealing the ordered processes beneath apparent market randomness, he offers traders and investors tools for understanding crowd behavior, timing market turns, and ultimately making independent decisions that distinguish successful participants from the crowd that inevitably loses.

I apologize for not understanding correctly. Your current explanations has made it clear that the issue was indeed 'edge' disappearing, in the sense of tradeable opportunity and non-tradeable opportunity.

I think universally traders agree that everybody has different styles and tastes when it comes to trading. One of the beautiful things about trading. And your style to me seems like very action-centric. You like volatility, big moves, uncertainty, fear among the markets. Because you have found a way to exploit that. But again, not many people who trade this way, like to trade News, or other volatile activities present in the market. I guess everybody has their own taste.

For me, I am very much the complete opposite. I always trade markets that are in equilibrium. However, that is subjective. There's no definitive description of balance or equilibrium in markets and it depends on your analysis as to how to reach that conclusion. But, balance for me represents not volatility but trend exhaustions or high-probability of continued balance (already established previously). In market profile terms, I am what people call Responsive Trader. Always fading the trend.

There are many approaches to this. For instance, market profile gives you tools like 'Value' to work with. This is good to start with, but I found out that there was too much discretion doing responsive trading this way. So, that's when I was introduced to 'Econophysics'

As I told previously, I don't actually use methods or tools from this field. However, this field shaped my current approach towards trading. It helped me achieve the following:
1. See the significance of Quantitative Analysis.
2. Applying cross-field concepts, like entropy, power-laws, analysis on different distribution types (e.g student-t) and even geometry
3. Understand the importance of a open mind-set.
4. Searching for unique perspectives on the market by applying different or custom techniques to data.

Gregory Zuckerman, the author who wrote "The Man Who Solved The Market", was quoted:
"If you and I look at a flower, we'll see it and say what a beautiful flower, if a bee sees a flower it can see the things we're unable to see and beyond that"

The point is: Multiple Perspectives can help you see the unseen. The fundamental philosophy for any analysis technique is Perspective. You are analyzing the same data, from the same market millions if not billions of people analyze each and every second. But, you are seeing more then what others can see. This creates all sorts of opportunities even for research if not practical trading applications

I won't give personal information regarding what techniques I do use, what I don't. But, Econophysics shaped my understanding and approach towards analyzing the market. It was the first field I came across that did something different from the rest of them. After reading about it, I went on a 2-3 months 'expedition' of sorts where I curiously read articles and papers regarding different techniques, un-till it came together slowly. I hope this was helpful to you.

Hi everyone!

Please note. I would really appreciate it if you would try to stick to the topic I started to find a serious way for myself or others who are interested in making a living from trading, without getting too caught up in all sorts of theories.

This topic is intended to help identify clear solutions (preferably proven ones), rather than general discussions about everything under the sun, which already abound here on the forum and is probably one of the reasons why, after so long, I still haven't made any real progress toward achieving my desired goals from trading.

Thank you!

Do you want to go back to making Euro160-180k a year ?

Then forget about Trading for a Living.

Start with Trading as a Business.

You need to find the capital for this business. This will take time maybe 5 to 10 years and needs a plan.



post#16

I use AI myself from time to time, but I would strongly advise against blindly relying on the quality of its results. It's quite good in many areas, but it's far from surpassing human competence. As with all AI systems, perplexity is not infallible and can, on occasion, generate inaccurate information or misinterpret sources.

Always verify important information with reliable sources and trust your own judgment. Don't rely solely on AI.

Yes, I completely agree. Why I like Perplexity is because it's a knowledge generation tool rather than a prosthetic compensating for intelligence and skills one neither has nor understands. One can use it for research and generate quite astounding things, and then verify the results independently. My partner and I use it extensively and daily. It's like having the world's largest library at hand, complete with a junior analyst to go through the sources and prepare custom made reports for you. Then it's a matter of getting to know the analyst, and their strengths and weaknesses, in order to manage them for maximum mutual benefit.

Too good to be true!



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Oh, no apologies needed at all. And thank you very much for a very rich and generous answer. Very thoughtful, deeply considered, and impressively self-aware.

To retribute I'd say that I rate and appreciate my trading work more than my most frequently quoted academic research paper. I believe I've got a complex relationship to trading and the markets and, like I've said earlier on this thread, fate got me to the markets and is keeping me there. And as you've correctly intuited, I'm all for maximal action, and gold futures is now the playground. I did a review of CME futures markets to trade at the end of 2Q, and gold didn't make it onto the list. With the recent review that has now changed.

Why not use your data science background to do some heavy lifting on what you've already been shown? Action? Rather than waiting indefinitely for a train that won't come?

I don't know if I am reading this right, or if it's etymology all over again, but have you published an academic paper? This has highly raised my interest. I would love to get a DOI so I can search for it. If I am comprehending this correctly, otherwise ignore my embarrassing request.

You're overall approach towards trading is what traders don't pay attention to. There's wisdom even in the simplest of lines. Reviewing markets, Market's Selection or the term most people are familiar with 'Screening Markets', is a vital part of trading. Not many people understand, but as you grow in trading, things like Risk Management, Portfolio Optimization, Screening Markets, Reviewing Trades etc all become a vital part for sustenance.

People usually think it's all about that one strategy. If x happens do y. If strategies could allowed to be so linear, we would not be trading in a market that is so non-linear. There's parts beyond one's strategy that every trader must encounter at some point during their trading.

Your approach seems to be long-term and admittedly this should be obvious as you are a 'cyclic trader' and cycles can be forecasted years from now depending on what is being accomplished. But you are also very versatile. Moving from markets to markets depending on what suits the strategy's needs. I personally only trade FX markets and this is partly due to the capital I have. Soon, my analysis will extend towards futures, commodities and equities. My daily routine is first scanning all available FX pairs and then filtering through them to find the best candidates. I have built specific tools for this like a TUI (Terminal User Interface) to aid the analysis and make the experience much smoother then running python scripts or scrambling through excel sheets all day.

I don't use AI and don't plan too, especially for markets

Yes I have Plummers books on my bookshelf, he also did a SOTA presentation which is on Youtube (terrible sound quality though) - unfortunately his works didn't influence me, although they are good and have remained on the bookshelf instead of in the bin

I'm going to look like a right know it all - but I've read virtually EVERYTHING of significance out there and pieced it all together and most importantly discarded the vast rubbish out there - so far nothing beats what I know

All those cycles appear and disappear, they come together at key points, but aren't regular enough to risk money on - but the key is cycles - just not as AI pulls out - Plummer is extremely close with his 17 yr cycle

Plummer just focuses on Fib ratios - Fib ratios are just a part of the story, as previously mentioned you need to add in the cube, squares, golden rectangles and all the ratios shown in that Canon of Proportion

Take the 2009 SP500 LOW - % fall was 57.69, 57.7% is Tangent 30 degrees of a circle and the inverse of 1.732, 1.732 is the diagonal of a cube and square root of 3, but isn't mentioned because its not fib related

At the end of the day all these markets will work out to certain cycles and geometry, you just have to find it

SP500 should stutter come Spring'26 then its down into the 4 year cycle - I think the Foundation for the study of cycles came out with that particular cycle decades ago