Cycle Theory and Algorithmic Trading Strategies: Exploiting Repetitive Patterns

The financial markets, like nature itself, pulsate with rhythms that repeat over time.

This article delves into the fascinating realm of cycle theory, equipping you with low-frequency algorithmic trading strategies to exploit these repetitive patterns.

The Duality of Cycles:

Within the market's symphony, two main types of cycles play their melodies:

• Exogenous Cycles: Driven by external factors like economic cycles, interest rates, or political events. These tend to be longer-term (years to decades).

• Endogenous Cycles: Born from internal market dynamics, like investor sentiment or trading algorithms. These cycles are often shorter-term (weeks to months).

The Hallmarks of a Cycle:

To qualify as a true market cycle, three characteristics must align:

• Recurrence: The pattern repeats itself over time, not just a one-off occurrence.

• Rhythm: The cycle exhibits a specific frequency, with a predictable interval between peaks and troughs.

• Amplitude: The magnitude of the price swings varies throughout the cycle, with clear peaks and valleys.

Hurst's Guiding Principles:

Harold Edwin Hurst, a statistical scientist, laid the foundation for cycle analysis with his seven Principles of Commonality:

1. Proportionality: Price changes are not random but related to their past values.

2. Persistence: Trends tend to persist, with upward (downward) trends more likely to be followed by further upward (downward) movements.

3. Clustering: Similar price changes tend to cluster together.

4. Alternation: Prices tend to alternate between periods of increasing and decreasing variability.

5. Limited additivity: The sum of short-term changes does not necessarily equal the long-term change.

6. Long-tail distributions: Extreme price changes occur more frequently than predicted by a normal distribution.

7. Equilibrium is never achieved: Markets are constantly in flux, never truly reaching a state of perfect equilibrium.

Building the Melody: Composite Waves

Just like musical chords are formed by combining notes, market cycles can be combined to create composite waves. Imagine a short-term cycle riding on top of a longer-term one, creating a more complex rhythmic structure.

Spatial Shifts: Left and Right Translation

Cycles don't always repeat perfectly. Left translation occurs when a peak or trough arrives earlier than expected, while right translation signifies a delayed arrival. Algorithmic models can incorporate these potential shifts for more accurate cycle identification.

The Conductor: Dominant Cycles

Sometimes, one cycle exerts a more powerful influence on the market than others. This dominant cycle influences the behavior of shorter-term cycles, giving traders valuable insights into potential turning points.

Decoding the Rhythm: Tools of the Trade

Several tools aid in cycle identification and analysis:

• Spectral analysis: Decomposes time series data to reveal hidden periodicities.

• Wavelet analysis: Identifies cycles at different frequencies simultaneously.

• Fibonacci retracements: Used to identify potential support and resistance levels based on historical cycles.

Remember:

Cycle theory is a complex and subjective field. Backtesting and rigorous risk management are crucial before deploying algorithmic strategies based on cycle analysis. Combine cycle analysis with other technical and fundamental factors for a well-rounded approach to low-frequency algorithmic trading.

Francisco F. De Troya

Algorithmic trading & derivatives professional.

Executive Chairman, Blockmas

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