Class: Quant::DominantCycleIndicators

Inherits:
IndicatorsProxy show all
Defined in:
lib/quant/indicators/dominant_cycle_indicators.rb

Overview

Dominant Cycles measure the primary cycle within a given range. By default, the library is wired to look for cycles between 10 and 48 bars. These values can be adjusted by setting the ‘min_period` and `max_period` configuration values in Config.

Quant.configure_indicators(min_period: 8, max_period: 32)

The default dominant cycle kind is the ‘half_period` filter. This can be adjusted by setting the `dominant_cycle_kind` configuration value in Config.

Quant.configure_indicators(dominant_cycle_kind: :band_pass)

The purpose of these indicators is to compute the dominant cycle and underpin the various indicators that would otherwise be setting an arbitrary lookback period. This makes the indicators adaptive and auto-tuning to the market dynamics. Or so the theory goes!

Instance Attribute Summary

Attributes inherited from IndicatorsProxy

#dominant_cycle, #indicators, #series, #source

Instance Method Summary collapse

Methods inherited from IndicatorsProxy

#<<, #attach, #dominant_cycle_indicator, #indicator, #initialize

Constructor Details

This class inherits a constructor from Quant::IndicatorsProxy

Instance Method Details

#acr ⇒ Object

Auto-Correlation Reversals is a method of computing the dominant cycle by correlating the data stream with itself delayed by a lag.



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# File 'lib/quant/indicators/dominant_cycle_indicators.rb', line 19

def acr; indicator(Indicators::DominantCycles::Acr) end

#band_pass ⇒ Object

The band-pass dominant cycle passes signals within a certain frequency range, and attenuates signals outside that range. The trend component of the signal is removed, leaving only the cyclical component. Then we count number of iterations between zero crossings and this is the ‘period` of the dominant cycle.



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# File 'lib/quant/indicators/dominant_cycle_indicators.rb', line 26

def band_pass; indicator(Indicators::DominantCycles::BandPass) end

#differential ⇒ Object

The Dual Differentiator algorithm computes the phase angle from the analytic signal as the arctangent of the ratio of the imaginary component to the real component. Further, the angular frequency is defined as the rate change of phase. We can use these facts to derive the cycle period.



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# File 'lib/quant/indicators/dominant_cycle_indicators.rb', line 38

def differential; indicator(Indicators::DominantCycles::Differential) end

#half_period ⇒ Object

Static, arbitrarily set period.



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# File 'lib/quant/indicators/dominant_cycle_indicators.rb', line 47

def half_period; indicator(Indicators::DominantCycles::HalfPeriod) end

#homodyne ⇒ Object

Homodyne means the signal is multiplied by itself. More precisely, we want to multiply the signal of the current bar with the complex value of the signal one bar ago



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# File 'lib/quant/indicators/dominant_cycle_indicators.rb', line 31

def homodyne; indicator(Indicators::DominantCycles::Homodyne) end

#phase_accumulator ⇒ Object

The phase accumulation method of computing the dominant cycle measures the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. The phase is then accumulated and the period is derived from the phase.



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# File 'lib/quant/indicators/dominant_cycle_indicators.rb', line 44

def phase_accumulator; indicator(Indicators::DominantCycles::PhaseAccumulator) end