MarketCycles
This package provides digital signal processing indicators developed by John F. Ehlers.
Currently the original indicators shared in his book: Cycle Analytics for Traders, Advanced Technical Trading Concepts are provided with intent to explore the DSP space and provide new intuitions based on the Ehlers framework.
Available Indicators
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Available Indicators
- Supersmoother
- Decycler
- Decycler Oscillator
- Band Pass Filter
- Hurst Coefficient
- HP-LP Roofing Filter
- Zero Mean Roofing Filter
- Roofing Filter
- Modified Stochastic
- Modified RSI
- Autocorrelation (Multiple Lag Matrix)
- Autocorrelation (Single Lag)
- Autocorrelation Periodogram
- Autocorrelation Reversals
- Adaptive RSI
- Adaptive Stochastic Indicator
- Adaptive CCI Indicator
- Adaptive Band Pass Filter
- Fisher Transform (Price)
-
TO DO
- Dominant Cycle - Fix DC Portion
- DFT Spectral Estimate - Fix MaxPwr Calculation
- Comb Filter Spectral Estimate
- Even Better SineWave Indicator
- Compute and Display Convolution
- Classic Hilbert Transformer
- Hilbert Transformer Indicator
- Dominant Cycle Using the Dual Differentiator Method
- Dominant Cycle Using the Phase Accumulation Method
- Dominant Cycle Using the Homodyne Method
- Fisher Transform to the Adaptive RSI Indicator
- SwamiCharts RSI
- SwamiCharts Stochastic
Usage
]
add https://github.com/flare9x/MarketCycles.jlEach indicator function requires an input of a single dimension array of Float64 type. Call @doc function_name to see the associated documentation for each specific indicator. A list of indicator function names can be found at the bottom of the page. Here is an exmaple of calling the @doc for AutoCorrelationReversals:
julia> @doc AutoCorrelationReversals
Autocorrelation Reversals - Equation 8-3
The indicated reversals are very sensitive to the smoothing of the price data.
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LPLength is made available as an indicator input to decrease or increase the number of
indicated reversals as desired.
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The AvgLength parameter is also made available as an indicator because this averaging also
impacts the number of indicated reversals.
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Care should be taken when increasing the value of this input because the lag of the
indicator increases in direct proportion to the increase of the value of the AvgLength.
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Typical delay of the indicator will be about three bars when the AvgLength parameter is set
to a value of 3.
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AutoCorrelationReversals(x::Array{Float64}; min_lag::Int64=1, max_lag::Int64=48,
LPLength::Int64=10, HPLength::Int64=48, AvgLength::Int64=3)::Array{Float64}Call the function as below for lags 1 to 48:
AutoCorrelationReversals(your_data,min_lag=1,max_lag=48,LPLength=10,HPLength=48,AvgLength=3)Below is an exmaple of calling the AutoCorrelationReversals reversals over dummy market data:
using MarketCycles
using Gadfly
# Generate dummy data
using Random
Random.seed!(1234)
n = 1000
op = 100.0 .+ cumsum(randn(n))
hi = op + rand(n)
lo = op - rand(n)
cl = 100.0 .+ cumsum(randn(n))
index = collect(1:1:length(cl))
for i = 1:n
if cl[i] > hi[i]
cl[i] = hi[i]
elseif cl[i] < lo[i]
cl[i] = lo[i]
end
end
# Apply autocorrelation reversals function
auto_cor_reversals = AutoCorrelationReversals(cl; min_lag=1, max_lag=48, LPPeriod=10, HPPeriod=48, AvgLength=3)
# Plot
white_panel = Theme(
panel_fill="white",
default_color="blue",
background_color="white"
)
p1 = plot(x=index,y=cl,Geom.line,
Guide.xlabel(nothing), Guide.ylabel("Price"), Guide.title("Dummy Data"),white_panel)
p2 = plot(x=index,y=auto_cor_reversals,Geom.line,Guide.xlabel("Time Index"),Guide.title("Autocorrelation Reversals"),white_panel)
out = vstack(p1,p2)
# Save Plot
draw(PNG("C:/Users/Andrew.Bannerman/Desktop/Julia/auto_correlation_reversals.png", 1500px, 800px), out)For the output:
Available Function Names
SuperSmoother, Decycler, Decycle_OSC, BandPassFilter, DominantCycle, HurstCoefficient, HPLPRoofingFilter,
ZeroMeanRoofingFilterK0, ZeroMeanRoofingFilterK1, RoofingFilterIndicator,
ModifiedStochastic, ModifiedRSI, AutoCorrelationIndicator, SingleLagAutoCorrelationIndicator, AutoCorrelationPeriodogram,
AutoCorrelationReversals, DFTS, AdaptiveRSI, AdaptiveStochastic, AdaptiveCCIFeel free to explore any of the functions with:
@doc SuperSmoother