Section 9 - Chapter 1 - Foundation of Cycle Theory

Section 9 - Chapter 1 - Foundation of Cycle Theory - Presented by Rohan Sharma - The CMT Coach - Chartered Market Technician CMT Level 1 Study Material - CMT Level 1 Chapter Wise Short Notes - CMT Level 1 Course Content - CMT Level 1 2025 Exam Syllabus Visit Site : www.learn.ptaindia.com and www.ptaindia.com

1. Chapter 1 - Foundations of Cycle Theory Section 9 – Cycle Analysis Presented By : This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

2. Agenda Foundations of Cycle Theory Cycle Characteristics Principles What Is a Dominant Cycle? Fixed Cycle Tools This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

3. Foundation of Cycle 🔹 Key Facts Fixed Cycles (Linear Cycles) • Follows a rigid, predictable pattern. • Events occur in a fixed order without deviation. • Often time-based or process-driven. • Example: The four seasons (Spring → Summer → Autumn → Winter). Sequences (Nonlinear Cycles) • Flexible, dynamic progression where steps may vary. • Events can occur in multiple possible orders. • Often dependent on external influences or decision-making. • Example: A business decision process where the next step depends on market response.

4. Foundation of Cycle Interpretation • Fixed cycles are ideal for routine-based, predictable systems (e.g., manufacturing, nature, and scheduled workflows). • Nonlinear sequences are adaptable and flexible, commonly seen in decision-making, complex systems, and AI processes . 🔍 Comparison & Application Context Fixed Cycle Example Nonlinear Sequence Example Nature Moon phases Climate change patterns Business Yearly financial reports Marketing strategies adjusting to trends Technology Software update cycles AI learning paths Biology Life cycle of a butterfly Evolution of species

5. Foundation of Cycle 📝 Cheat Sheet Feature Fixed Cycles (Linear) Sequences (Nonlinear) Predictability High (Repeats the same way) Low (Varies based on conditions) Flexibility None (Follows strict order) High (Steps can change) Examples Seasons, Clock time, Production lines Business strategies, Learning paths, Evolution Dependency Fixed structure Context-dependent Change over time? No (Cycle repeats identically) Yes (Path may change)

6. Cycle Characteristics Key Facts on Cycle Characteristics 1. Repetition – Cycles follow a recurring pattern over time. 2. Predictability – Many cycles have a regular, measurable interval (e.g., seasons, tides). 3. Phases/Stages – Cycles consist of distinct phases that transition smoothly or abruptly. 4. Duration – Some cycles are fixed (e.g., the lunar cycle), while others vary (e.g., business cycles). 5. Feedback Loops – Some cycles self-regulate through positive or negative feedback (e.g., ecological cycles). 6. External Influences – Cycles can be disrupted or altered by external factors (e.g., climate affecting seasonal cycles). 7. Direction – Can be linear (progressive change, no return) or circular (repetitive and looping).

7. Cycle Characteristics

8. Cycle Characteristics Cycle Characteristics: Period, Amplitude & Phase 1. Period (Time Duration of a Cycle) • The period of a cycle refers to the time it takes for a full cycle to complete from one peak to the next peak or one trough to the next trough. • Measured in days, weeks, months, or years. • Short-term cycles (e.g., daily stock price movements) vs. long-term cycles (e.g., economic recessions). • Example: A 4-year stock market cycle follows economic expansions and contractions .

9. Cycle Characteristics Cycle Characteristics: Period, Amplitude & Phase 2. Amplitude (Magnitude or Strength of a Cycle) • The amplitude represents the height (intensity) of price fluctuations within a cycle. • A higher amplitude means stronger price movements (higher volatility). • A lower amplitude indicates smaller price fluctuations (low volatility). • Example: The crypto market has high amplitude cycles, with extreme price swings compared to stable blue-chip stocks.

10. Cycle Characteristics Cycle Characteristics: Period, Amplitude & Phase 3. Phase (Timing or Alignment of a Cycle) • Phase defines the position of the cycle relative to a reference point (i.e., when it starts or shifts). • Different market sectors or assets can have out-of-phase cycles (not moving in sync). • Example: Gold and stocks often move in opposite phases—gold rises when the stock market falls . 🔹 Summary Table Characteristic Definition Example in Trading Period Time it takes for one full cycle to complete 4-year economic cycle Amplitude Strength of price movements High amplitude in volatile assets like crypto Phase Position of the cycle relative to others Gold rising when stocks fall (inverse phase)

11. J.M. Hurst's Cycle Theory 🔹 Key Facts on Hurst’s Cycle Theory 1. Cycles Exist in All Markets – Price movements are influenced by multiple overlapping cycles. 2. Harmonic Ratios – Larger cycles are often harmonically related to smaller cycles (e.g., 2:1 or 3:1 ratios). 3. Summation Principle – Market prices result from the sum of multiple cycles of different lengths. 4. Nominality Principle – Certain cycle lengths appear consistently across markets (e.g., 20-week, 40-week cycles ).

12. J.M. Hurst's Cycle Theory

13. J.M. Hurst's Cycle Theory 🔹 Key Facts on Hurst’s Cycle Theory 5 . Synchronicity – Related cycles tend to reach troughs at the same time, influencing market movements. 6. Proportionality – Longer cycles tend to have greater amplitude (larger price swings). 7. FLD (Future Line of Demarcation) – A tool used to forecast price movements based on past cycle behavior. 8. Translation – Cycles may shift forward or backward due to external market forces.

14. J.M. Hurst's Cycle Theory Harmonic

15. J.M. Hurst's Cycle Theory Cheat Sheet: Hurst’s Cycle Theory Principle Description Example in Trading Cycles Exist Market moves in recurring patterns Stock market bull & bear cycles Harmonic Ratios Cycles often relate by factors of 2 or 3 A 40-week cycle has two 20-week sub-cycles Summation Price is the sum of all active cycles Multiple timeframes influencing a stock’s trend Nominality Fixed cycle lengths appear in different assets 20-day and 80-day cycles in stocks & forex Synchronicity Similar cycles bottom together Major sell-offs across markets Proportionality Longer cycles = Bigger price moves Yearly cycles lead to bigger trends than daily cycles FLD Tool Predicts price targets using past cycles Used to anticipate reversals Translation Cycle peaks/troughs may shift due to market influences Unexpected news delaying a cycle low

16. J.M. Hurst's Cycle Theory Interpretation of Hurst’s Cycle Theory • Practical Use: Helps traders anticipate future price movements by analyzing overlapping cycles. • Multi-Cycle Impact: Shorter cycles influence daily price action, while longer cycles shape trends. • Cycle Shifts: External factors like economic policies or news events can cause cycles to shift. • Ideal for Swing & Position Traders: Hurst’s analysis is useful for medium- to long-term traders seeking optimal entry/exit points.

17. J.M. Hurst's Cycle Theory To apply Hurst’s Cycle Theory, let's go step by step: 1. Choose an Asset/Class – Are you analyzing stocks, forex, crypto, commodities, or indices? 2. Select a Timeframe – Do you want short-term (days/weeks), medium-term (months), or long-term (years) cycle analysis? 3. Identify Cycles – We can look at dominant cycles (e.g., 20-day, 40-week) and apply Hurst’s FLD tool or moving averages. 4. Interpret Trends – Determine cycle peaks/troughs, market phases, and entry/exit points. 5. Consider External Factors – News, earnings, economic policies may shift cycles (translation effect).

18. Dominant Cycle What Is a Dominant Cycle? A dominant cycle is the most influential and recurring cycle within a dataset, financial market, or natural system. It has the strongest effect on price movements and helps traders identify key turning points. 🔹 Key Characteristics of a Dominant Cycle 1 . Repetition – It recurs consistently over time. 2. Strong Amplitude – It has a noticeable impact on price action. 3. Clear Periodicity – The time between peaks or troughs remains relatively stable. 4. Influence on Other Cycles – Other smaller cycles interact with or harmonically relate to it. 5. Can Shift Due to External Forces – Events like economic policies, news, or large market participants can slightly alter the cycle.

19. Dominant Cycle How to Identify a Dominant Cycle in Technical Analysis 1. Fourier Transform & Spectral Analysis – Identifies the strongest cycles in historical price data. 2. Moving Averages & Peak Analysis – Detects repeating price patterns over specific periods. 3. Hurst’s Future Line of Demarcation (FLD) – Helps forecast future price movements based on dominant cycles. Elliott Wave & Fibonacci Timing – Used to identify dominant market cycles in wave patterns. Interpretation & Trading Use • Traders use dominant cycles to predict when markets rise or fall. • Short-term traders focus on daily/weekly cycles (e.g., 20-day cycles in forex). • Long-term investors track macro cycles (e.g., the 10-year economic cycle). • Combining Hurst’s Cycle Theory, Moving Averages, and Oscillators improves timing accuracy .

20. Dominant Cycle Example of Dominant Cycles in Markets . Market Example of Dominant Cycle Stock Market 4-Year Presidential Cycle Crypto Bitcoin’s 4-Year Halving Cycle Forex 80-day & 20-day cycles for trend reversals Commodities Seasonal Agricultural & Oil Price Cycles Economy Kondratieff Wave (50-60 years), Business Cycles (5-10 years)

21. Fixed Cycle Tools: Spectrograms & Wavelet Diagrams 🔹 Spectrograms What is a Spectrogram? A spectrogram is a visualization of how the frequency content of a signal changes over time. It helps detect fixed cycles by showing which frequencies (cycles) are dominant at different points in time. How It Works in Cycle Analysis • Converts price/time data into a frequency spectrum. • Identifies dominant cycles that persist over time. • Helps spot shifts in market rhythms due to economic events or volatility spikes. Example Use in Trading • Detects stable cycles in stock prices or forex pairs. • Used in Harmonic Analysis to find repeating price patterns. • Helps in seasonality studies for commodities or indices .

22. Fixed Cycle Tools: Spectrograms & Wavelet Diagrams 🔹 Spectrograms What is a Spectrogram? A spectrogram is a visualization of how the frequency content of a signal changes over time. It helps detect fixed cycles by showing which frequencies (cycles) are dominant at different points in time. How It Works in Cycle Analysis • Converts price/time data into a frequency spectrum. • Identifies dominant cycles that persist over time. • Helps spot shifts in market rhythms due to economic events or volatility spikes. Example Use in Trading • Detects stable cycles in stock prices or forex pairs. • Used in Harmonic Analysis to find repeating price patterns. • Helps in seasonality studies for commodities or indices .

23. Fixed Cycle Tools: Spectrograms & Wavelet Diagrams

24. Fixed Cycle Tools: Spectrograms & Wavelet Diagrams

25. Fixed Cycle Tools: Spectrograms & Wavelet Diagrams 🔹 Wavelet Diagrams What is a Wavelet Diagram? A wavelet diagram (or wavelet transform) is an advanced tool that analyzes both fixed and variable cycles. Unlike spectrograms, wavelets handle non-stationary signals, meaning they work well when cycles change over time. How It Works in Cycle Analysis • Breaks down a time series into different cycle components. • Detects short-term & long-term cycles simultaneously. • Highlights cycle shifts (Hurst’s "Translation Effect"). Example Use in Trading • Stock Market: Identifies major and minor trend cycles. • Crypto Trading: Captures irregular halving effects in Bitcoin. • Forex: Analyzes multi-timeframe interactions between short and long cycles.

26. Fixed Cycle Tools: Spectrograms & Wavelet Diagrams Cheat Sheet: Spectrogram vs. Wavelet Diagram Feature Spectrogram Wavelet Diagram Cycle Type Fixed, repeating cycles Fixed & changing cycles Time Adaptability Less adaptable to cycle shifts Highly adaptable Best For Stable market patterns Markets with shifting trends Analysis Type Frequency-based Time-frequency-based Example Use Seasonal stock trends Crypto boom-bust cycles

27. Fixed Cycle Tools: Spectrograms & Wavelet Diagrams Interpretation & Application in Trading 1. Spectrograms are best for stable, recurring cycles (e.g., economic reports, election cycles). 2. Wavelets help in nonlinear, shifting cycles (e.g., Bitcoin’s halving, financial crises). 3. Combining both tools provides deeper insights into price movement patterns.

28. Fixed Cycle Tools: Fisher Transform 🔹 What is the Fisher Transform? The Fisher Transform is a mathematical indicator that converts non-normally distributed price data into a Gaussian (normal) distribution. This makes it easier to identify sharp turning points and fixed cycles in market data. Developed by John F. Ehlers, the Fisher Transform is commonly used in technical analysis to detect cycle peaks and troughs with increased sensitivity. 🔹 How the Fisher Transform Works in Cycle Analysis 1. Transforms price data into a Gaussian distribution (bell curve). 2. Enhances cycle reversals, making peaks & troughs more visible. 3. Removes noise, making it easier to detect fixed cycles. 4. Amplifies extreme values, allowing traders to spot overbought/oversold conditions.

29. Fixed Cycle Tools: Fisher Transform

30. Fixed Cycle Tools: Fisher Transform How to Use the Fisher Transform in Trading 1. Identifying Fixed Cycles in Markets • Since Fisher Transform sharpens turning points, it helps traders spot regular market cycles (e.g., 20-day, 80-day cycles). • Works well with Hurst Cycle Theory by confirming expected price reversals. 2. Entry & Exit Signals • Buy Signal 📈: When the Fisher Transform crosses above its signal line from an extreme low. • Sell Signal 📉: When the Fisher Transform crosses below its signal line from an extreme high. 3. Filtering Market Noise • Unlike moving averages, Fisher Transform reduces false signals by highlighting the most significant turning points.

31. Fixed Cycle Tools: Fisher Transform Cheat Sheet: Fisher Transform in Cycle Analysis Feature Fisher Transform Purpose Converts price into a Gaussian cycle pattern Best For Detecting fixed cycle peaks & troughs Type of Cycles Works well with fixed & harmonic cycles Main Advantage Sharpens reversal points & reduces noise Signal Type Overbought/Oversold turning points Ideal Pairing Hurst Cycle Theory, Moving Averages, RSI Interpretation in Technical Analysis Use in Trend Markets ✅ Helps traders identify cycle-based pullbacks for re-entry. Use in Range-Bound Markets ✅ Works well in mean-reverting cycles (e.g., Bollinger Bands). Avoid False Signals 🚨 Fisher Transform reacts quickly to price changes, so combining it with other cycle tools (e.g., Spectrograms, Wavelets, MACD) improves accuracy.jymjyk

32. Next Chapter 2 – Common Cycle Section 9 – Cycle Analysis Presented By : This Content is Copyright Reserved Rights Copyright 2025@PTAIndia