Skip to content
Digital Signal Processing Indicators For Market Data
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
examples
src
test
.codecov.yml
.gitignore
.travis.yml
LICENSE.md
README.md
REQUIRE
appveyor.yml

README.md

Build Status Coverage Status codecov.io

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

  • 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
    • Fisher Transform (Price)
  • TO DO

    • Dominant Cycle - Fix DC Portion
    • DFT Spectral Estimate - Fix MaxPwr Calculation
    • Comb Filter Spectral Estimate
    • Adaptive CCI
    • Adaptive Band Pass Filter
    • 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.jl

Each 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.
 ≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡

  LPLength is made available as an indicator input to decrease or increase the number of
indicated reversals as desired.
 ≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡

  The AvgLength parameter is also made available as an indicator because this averaging also
impacts the number of indicated reversals.
 ≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡

  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.
 ≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡

  Typical delay of the indicator will be about three bars when the AvgLength parameter is set
to a value of 3.
 ≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡≡

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
srand(121)
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,LPLength=10,HPLength=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"), Guide.ylabel("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:

John Ehlers Autocorrelation Reversals

Available Function Names

    SuperSmoother, Decycler, Decycle_OSC, BandPassFilter, DominantCycle, HurstCoefficient, HPLPRoofingFilter,
    ZeroMeanRoofingFilterK0, ZeroMeanRoofingFilterK1, RoofingFilterIndicator,
    ModifiedStochastic, ModifiedRSI, AutoCorrelationIndicator, SingleLagAutoCorrelationIndicator, AutoCorrelationPeriodogram,
    AutoCorrelationReversals, DFTS, AdaptiveRSI, AdaptiveStochastic

Feel free to explore any of the functions with:

@doc SuperSmoother
You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.