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Trading Education · Cyclic Analysis
Markets keep time. Here is how to read the clock.
Price is not one wave — it is many, layered on top of each other, each with its own length and rhythm. This guide traces cyclic analysis from sunspots and harvests to the trading desk, introduces the thinkers who built the field, and walks through the Phaztech indicator suite that puts these ideas on your TradeStation chart.
For educational purposes only · Not financial adviceStart here
What cyclic analysis is
Cyclic analysis is the study of recurring, wave-like fluctuations — patterns that rise and fall on a roughly regular beat rather than moving randomly or in a straight line.
The core claim is simple and old: many things in nature and in human affairs oscillate. Day follows night, tides come in and go out, populations of predators and prey chase each other up and down, economies expand and contract. Where a variable returns to a similar state at roughly regular intervals, we call that a cycle, and we can describe it with three numbers: its period (how long one repetition takes), its amplitude (how large the swing is), and its phase (where it currently sits in its rise-and-fall).
In financial markets the idea is that a price chart is not a single tidy wave but a sum of multiple cycles of different lengths — a short choppy rhythm riding on a medium swing riding on a long tide — all added together into the one messy line you actually see. Cyclic analysis is the craft of pulling those individual rhythms back apart, measuring each one, and using them to anticipate where the next turn is likely to fall. It is a timing discipline first and foremost: it aims to answer “when,” and only loosely “how far.”
The essential picture
Think of a musical chord. It sounds like one tone, but it is actually several notes played together. Markets work the same way — the price you see is the chord; cyclic analysis is the ear that separates it back into its individual notes.
Origins
A brief history of cycles
Humans have organized life around cycles for as long as there has been agriculture. Ancient calendars tracked the seasons; Egyptian society ran on the annual flooding of the Nile; sailors learned the tides. These are the oldest and most obvious cycles — driven by the rotation of the Earth, the orbit of the Moon, and the tilt of the planet around the Sun. The intellectual leap that led to modern cyclic analysis was the suspicion that far less obvious things — crop prices, business activity, even wars — might also move on hidden, measurable rhythms.
1801
Herschel links sunspots to wheat
Astronomer William Herschel noticed that the price of wheat seemed to rise and fall with the number of sunspots, proposing that solar activity influenced climate, harvests, and therefore grain prices — an early, controversial claim that a market moved with a natural cycle.
1870s
Jevons and the sunspot business cycle
Economist William Stanley Jevons formalized the idea, arguing that the roughly 11-year solar cycle drove agricultural cycles and, through them, commercial crises. The specifics didn’t hold up, but the framing — that economies have periodic rhythms worth measuring — stuck.
1920s
Kondratieff and the long wave
Russian economist Nikolai Kondratieff described “long waves” of roughly 45–60 years in prices and production, tied to major technological epochs. Joseph Schumpeter later folded this into a layered model of the economy alongside shorter cycles named for Kitchin, Juglar, and Kuznets.
1941
Dewey founds the study of cycles
Edward R. Dewey — a former Chief Economic Analyst at the U.S. Department of Commerce tasked by President Hoover with explaining the Great Depression — founded the Foundation for the Study of Cycles. He catalogued rhythms in stock prices, wheat, cotton, wildlife populations, precipitation, tree rings, even the frequency of wars, and championed the idea that unrelated fields shared common cycle lengths.
1970
Hurst brings signal processing to price
Engineer J.M. Hurst published The Profit Magic of Stock Transaction Timing, applying rigorous cyclic and signal-processing thinking to stock charts and laying out a set of named principles that still underpin the field — and this indicator suite.
Cyclic analysis has always attracted both serious researchers and overreach. The honest position — the one this guide takes — is that cycles are real and useful as a tendency and a timing aid, never as a guarantee. More on that caveat throughout.
Why it matters beyond markets
The far-reaching impact of cycles
Part of what makes cyclic analysis compelling is how widely the same idea applies. Cycles show up across domains that have nothing obvious to do with one another — and the same measurement tools work on all of them.
Climate & weather
Natural rhythms
Day/night and seasonal cycles, the ~11-year solar cycle, the El Niño–Southern Oscillation reshuffling global weather every few years, and Milankovitch orbital cycles pacing ice ages over tens of thousands of years.
Agriculture
Harvest & herd
Planting and harvest seasons, multi-year commodity gluts and shortages (the “hog cycle” of overproduction and scarcity), and predator–prey population swings like the classic lynx–hare cycle.
Economics
Boom & bust
The business cycle of expansion and recession, inventory restocking cycles, credit and debt cycles, and the long technological waves that carry decades of growth and stagnation.
Geopolitics
Conflict & generations
Researchers from Dewey onward have argued for periodicity in the incidence of war and social upheaval, and generational theories describe recurring ~80-year cycles of crisis and renewal in societies.
Human biology
Internal clocks
Circadian rhythms governing sleep and hormones, and longer biological and behavioral cycles — a reminder that “cycles” are wired into the participants of every market, not just the market itself.
Finance
Price & sentiment
The domain this guide zooms into: market cycles of fear and greed, seasonal patterns, the four-year cycle, and the overlapping price rhythms that cyclic indicators are built to isolate.
Dewey’s big claim
The Foundation for the Study of Cycles found that seemingly unrelated phenomena — commodity prices, business activity, wildlife abundance — sometimes shared the same cycle lengths. Whether or not every such link is real, the observation reframed cycles as a general property of complex systems, not a quirk of any one field.
The market’s clocks
The types of cycles in financial systems
“The market cycle” is really shorthand for many overlapping cycles, each driven by a different force and running on a different clock. Understanding which is which keeps you from mistaking, say, an options-expiration wobble for a change in the primary trend. Here are the ones that matter most.
Decades → years
The economic (business) cycle
The macro backdrop: expansion, peak, contraction, trough. Classically decomposed into the Kitchin inventory cycle (~3–5 yrs), the Juglar investment cycle (~7–11 yrs), the Kuznets infrastructure cycle (~15–25 yrs), and the Kondratieff long wave (~45–60 yrs).
Years → months
The market cycle
The four emotional phases of price: accumulation, mark-up, distribution, mark-down — the bull-and-bear rotation driven by the swing between fear and greed. It leads the economy, turning before the news confirms why.
Quarterly
The earnings cycle
Corporate reporting runs on a quarterly cadence, producing recurring “earnings seasons” that inject volatility and reprice expectations on a predictable calendar four times a year.
Monthly / weekly
The options cycle
Standard options expirations, monthly “OpEx,” and quarterly triple/quadruple witching create recurring pockets of hedging flow, pinning, and volatility around known expiration dates.
Seasonal
Calendar & seasonal cycles
“Sell in May,” the year-end Santa rally, tax-driven flows, and genuine commodity seasonality (heating demand, harvest supply) that repeats on an annual clock — the terrain Larry Williams built his seasonal indexes on.
~4 years
The presidential / election cycle
A well-studied ~4-year rhythm in U.S. equities tied to the political and fiscal calendar, with historically weaker early years and stronger pre-election years.
Variable
Price / dominant cycles
The measurable oscillations inside the chart itself — the short, medium, and long price rhythms that spectral analysis isolates and that most of the indicators here are built to track.
Variable
Sentiment & liquidity cycles
The tides of risk appetite, credit availability, and positioning that swell and drain beneath price — slower-moving currents that set the stage for the faster cycles above.
These cycles don’t operate in isolation — they nest and interfere with one another, exactly as Hurst described. A short cycle turning up inside a long cycle that is turning down produces a very different trade than the same short cycle turning up with the tide behind it. Reading them together is the whole game.
The field’s architects
Cyclic analysis in the markets: the key thinkers
The application of cycles to trading was built by a handful of engineers, analysts, and researchers, each contributing a distinct method. Together they form the intellectual lineage behind modern cycle indicators — including this suite.
GC1910
George Campbell
The bandpass filter · origin point
An American mathematician and engineer at AT&T and Bell Labs, Campbell invented the electric wave filter in 1910 to solve a telephone problem: running several conversations down one wire at once. His circuits let a single narrow band of frequencies pass while blocking everything faster and slower.
That invention — the bandpass filter — is the mathematical heart of nearly every modern cycle indicator. Decades later it would be pointed at price data instead of phone lines to isolate one market cycle from all the others.
JH1970
J.M. Hurst
The father of modern cyclic trading
An aerospace engineer, Hurst applied signal-processing rigor to the stock market in his 1970 classic The Profit Magic of Stock Transaction Timing and his later Cyclitec course. He argued that price is the summation of several harmonically related cycles plus a trend, and codified a set of principles — summation, harmonicity, synchronicity, proportionality, variation, nominality, commonality — that still define the field.
He also devised the FLD (Future Line of Demarcation) and the centered-average envelope, both of which appear directly in this suite. If cyclic analysis has a single foundational author, it is Hurst.
BM1990s
Brian Millard
Centered averages & channel analysis
A British analyst who extended and popularized Hurst’s work, Millard focused on the centered moving average — shifting an average backward by half its length so it sits in the middle of the data it describes rather than lagging behind it. He built channel and envelope techniques around this idea, using odd-length averages so the centered line lands exactly on a bar.
His methodology is the direct basis for the Cycle Channel indicator in this suite.
JE1990s+
John Ehlers
Digital signal processing for traders
An electrical engineer with a background in radar and DSP, Ehlers is the person who systematically brought signal-processing tools — bandpass filters, the Hilbert transform, adaptive filters, and MESA (Maximum Entropy Spectral Analysis) — into practical trading indicators. His books, including Rocket Science for Traders, Cybernetic Analysis for Stocks and Futures, and Cycle Analytics for Traders, are the modern engineering canon of the field.
RM1981
Raymond Merriman
Cycles & geocosmic correlation
Editor of the MMA Cycles Report since 1981 and author of the five-volume Ultimate Book on Stock Market Timing, Merriman is a rigorous cataloguer of market cycles — defining them statistically by their typical duration and the reliability of their troughs. He is also a leading figure in financial astrology, empirically studying correlations between planetary and solar/lunar cycles and market turning points.
Whatever one makes of the geocosmic side, his disciplined framework for classifying cycles by average length and trough consistency has shaped how practitioners think about cycle reliability.
TM1970s+
Tom McClellan
Breadth, intermarket & physical cycles
Best known for extending his parents’ McClellan Oscillator — the classic market-breadth tool — Tom McClellan (a West Point-trained engineer) built an extensive body of work synthesizing the four-year presidential cycle, intermarket lead–lag relationships, and physical cycles. His research showing that gold’s ~13½-month rhythm tracks the Moon’s apogee–perigee cycle is a well-known example of anchoring a market cycle to a real astronomical clock.
His contribution is a practical, evidence-driven insistence that cycles and leading indicators be tested against the data.
LW1970s+
Larry Williams
Seasonality & cycle timing
A legendary trader and author (of, among many, The Right Stock at the Right Time), Williams built some of the earliest seasonal indexes in the 1970s, quantifying how often a market closes higher in a given week or month across decades of history. He mapped the four-year cycle, decade-year patterns, and the recurring pressures of harvests, tax flows, and inventory onto price.
His enduring lesson is to quantify a seasonal or cyclic edge — to state exactly what percentage of the time it has held — rather than trade on a vague sense of rhythm.
DKB1987
David Knox Barker
The long wave & Fibonacci cycles
Founder of Long Wave Dynamics and author of The K Wave, Barker is a leading authority on the Kondratieff long wave and its subdivisions. He developed the Market Cycle Dynamics approach, nesting shorter market cycles inside the long wave and organizing them by Fibonacci relationships to project where each degree of cycle sits within the greater whole.
His work is the macro bookend of the field — a reminder that the short price cycles on your chart are the smallest gears inside a much larger economic clock.
LvT2014
Lars von Thienen
Dynamic cycle detection
An engineer and trader, von Thienen advanced the practical detection of cycles in his Decoding the Hidden Market Rhythm series and the WhenToTrade tools. Founder and CEO of a German-based knowledge management company and board member of the Foundation For the Study of Cycles.
The theoretical spine
Hurst’s principles, in plain English
Because Hurst’s principles are wired directly into these indicators, they are worth knowing by name. Everything the suite measures — the doubling cycle lengths, the tolerance windows, the confluence signals — traces back to one of these.
Principle 1
Summation
Price at any instant is the sum of several cycles of different lengths, plus a trend. Bandpass filters run this in reverse — pulling the individual cycles back out.
Principle 2
Harmonicity
Cycle lengths aren’t random; they cluster around small whole-number ratios, most often a simple doubling. This is why the default bands run 10, 20, 40, 80, 160, 320, 640.
Principle 3
Synchronicity
When several cycles reach a trough (or peak) at the same time, they reinforce each other into a stronger, more reliable turn. This is confluence — the highest-value signal.
Principle 4
Variation
No real cycle repeats with perfect precision — its length and size drift around an average. This is why the suite validates every fresh measurement instead of trusting it blindly.
Principle 5
Nominality
Despite that drift, each cycle has a typical “nominal” length useful as a reference model — exactly what the suite’s default cycle lengths are.
Principle 6
Proportionality
A cycle’s amplitude tends to scale with its length — longer cycles produce bigger price swings. You’ll see this when comparing the short and long bands.
A seventh principle, commonality, holds that these cyclic characteristics are shared across essentially all freely traded markets — which is why one toolkit can be pointed at stocks, futures, or crypto alike.
J.M. Hurst · Methodology
Hurst’s method in practice
Hurst’s principles are the theory; his lasting contribution was turning them into a repeatable charting routine a trader could actually follow. That routine rests on four pillars — a standard model of cycle lengths, marking the cyclic highs and lows on the chart, reading where those turns nest together, and then proving a turn has happened with the FLD and the VTL. This suite is, in effect, that routine automated.
1
The nominal model — one set of lengths for every market
Studying decades of price data across many different markets, Hurst found that the same cycle lengths kept recurring — and that they were harmonically related, each roughly double the one below it. He organized these recurrent lengths into a standard reference he called the nominal model: an idealized ladder of periods (in days, weeks, months, and years) that a trader starts from on any instrument, then verifies and fine-tunes against the specific market in front of them.
Because these lengths are nominal — typical, not exact — the real measured cycle is expected to drift a little around each rung (the Principle of Variation). The suite encodes exactly this idea: its default bands of 10, 20, 40, 80, 160, 320, and 640 are a nominal model, and every indicator continuously measures the live cycle against it.
2
Charting the highs and lows for every cycle
For each length in the nominal model, Hurst marked that cycle’s turning points — its highs and lows — directly onto the price chart, working from the real swing highs and lows in the price action. Do this for all the lengths at once and you get a stacked picture of where every cycle is turning. In this suite that hand-charting becomes the Cycle Diamonds: one row of diamonds per cycle length, shortest nearest price, each marking that cycle’s projected trough or peak with a whisker band for its natural variation.
3
Nesting — where the real turning points hide
The single most important thing Hurst looked for was nesting: moments when the lows of several different cycle lengths are all due at the same time. When a short, a medium, and a long cycle all reach a trough together, they reinforce one another into a much stronger, more reliable bottom than any one of them would make alone — the Principle of Synchronicity in action. Hurst timed his entries to these nests of lows and his exits to nests of highs.
On the chart above, that is what you are hunting for: several diamonds of different colors stacking up at roughly the same bar. One diamond alone is a hint; a nest of them is a case. It is the reason the whole suite plots every cycle at once instead of just the dominant one.
4
Proving the turn — the FLD and the VTL
Marking where a low is expected is only half the job; Hurst wanted confirmation that a cyclic low or high had actually occurred. He used two tools for that proof.
The FLD (Future Line of Demarcation) is a moving average of price displaced forward in time by half of its cycle’s period. Because it is shifted into the future, a price/FLD crossover is a clean confirmation signal: price crossing above the FLD confirms a cyclic trough has passed, crossing below confirms a peak. The geometry of that crossing also projects a rough price target — the move away from the FLD tends to mirror the move into it — which is the “how far” that pairs with the diamonds’ “when.”
The VTL (Valid Trend Line) is a trendline drawn across two consecutive cyclic troughs (or two consecutive peaks) of a given cycle. As long as price holds above an up-sloping VTL the cycle is intact; when price breaks the VTL, Hurst treated it as valid proof that the next-longer cycle has turned down — the trough or peak is confirmed in. Used together, an FLD cross and a VTL break turn a projected turning point into a verified one, so you are acting on a turn the market has confirmed rather than one you are merely hoping for.
Why this matters for the toolkit
These four pillars map one-to-one onto the indicators: the nominal model is every tool’s default cycle set, the charted highs and lows are the Cycle Diamonds, nesting is the confluence you read across Waves and Diamonds, and the FLD + VTL proof is the FLD & Targets indicator. What Hurst did by hand over a weekend, the suite recomputes on every bar.
Sources
References & further reading
Foundational texts by the thinkers profiled above, plus the sources consulted while writing this guide. Indicator descriptions and screenshots are drawn from Phaztech’s own indicator documentation and charts.
Foundational works
- J.M. Hurst — The Profit Magic of Stock Transaction Timing (1970); the Cyclitec / “Hurst Cycles” course.
- John F. Ehlers — Rocket Science for Traders, Cybernetic Analysis for Stocks and Futures, Cycle Analytics for Traders.
- Brian J. Millard — Channel Analysis and related works on centered-average channels.
- Raymond A. Merriman — The Ultimate Book on Stock Market Timing (Vols. 1–5); The Gold Book.
- Larry Williams — The Right Stock at the Right Time.
- Lars von Thienen — Decoding the Hidden Market Rhythm (WhenToTrade).
- David Knox Barker — The K Wave (Long Wave Dynamics).
- Edward R. Dewey & Og Mandino — Cycles: The Mysterious Forces That Trigger Events.
Sources consulted
- Foundation for the Study of Cycles — cycles.org (founders, history, sunspot cycles).
- Wikipedia — Edward R. Dewey; Foundation for the Study of Cycles.
- Merriman Market Analyst — mmacycles.com (MMA Cycles Report; biography).
- McClellan Financial Publications — mcoscillator.com (McClellan Oscillator; presidential & lunar cycles).
- Lars von Thienen interviews & WhenToTrade — whentotrade.com, lars.cycles.org (Larry Williams & McClellan interviews).
- Long Wave Dynamics / Market Cycle Dynamics — marketcycledynamics.com (David Knox Barker; the K-Wave).
- StockCharts — Williams Cycle Forecast; Larry Williams seasonal work.