prelminary rolling acf function

still needs much further testing

src/Indicators.jl

@@ -3,7 +3,7 @@ VERSION >= v"0.4.0" && __precompile__(true)
 module Indicators
 
 export
-    runmean, runsum, runvar, runsd, runcov, runcor, runmax, runmin, runmad, runquantile,
+    runmean, runsum, runvar, runsd, runcov, runcor, runmax, runmin, runmad, runquantile, runacf,
     wilder_sum, mode, diffn, #lag,
     sma, trima, wma, ema, mma, kama, mama, hma, swma, dema, tema, alma, zlema,
     mlr_beta, mlr_slope, mlr_intercept, mlr, mlr_se, mlr_ub, mlr_lb, mlr_bands, mlr_rsq,

src/run.jl

@@ -1,3 +1,5 @@
+import Temporal.acf  # used for running autocorrelation function
+
 @doc doc"""
 Lagged differencing
 
@@ -96,7 +98,7 @@ function runsum(x::Array{Float64}; n::Int64=10, cumulative::Bool=true)::Array{Fl
             out[i] = sum(x[i-n+1:i])
         end
     end
-	return out
+    return out
 end
 
 @doc doc"""
@@ -216,7 +218,7 @@ function runcor(x::Array{Float64}, y::Array{Float64}; n::Int64=10, cumulative::B
 end
 
 @doc doc"""
-Compute the running or rolling maximum of an array.
+Compute the running or rolling maximum of an array
 
 `runmax(x::Array{Float64}; n::Int64=10, cumulative::Bool=true, inclusive::Bool=true)::Array{Float64}`
 """ ->
@@ -253,7 +255,7 @@ function runmax(x::Array{Float64}; n::Int64=10, cumulative::Bool=true, inclusive
 end
 
 @doc doc"""
-Compute the running or rolling minimum of an array.
+Compute the running or rolling minimum of an array
 
 `runmin(x::Array{Float64}; n::Int64=10, cumulative::Bool=true, inclusive::Bool=true)::Array{Float64}`
 """ ->
@@ -290,10 +292,10 @@ function runmin(x::Array{Float64}; n::Int64=10, cumulative::Bool=true, inclusive
 end
 
 @doc doc"""
-Compute the running/rolling quantile of an array.
+Compute the running/rolling quantile of an array
 
 `runquantile(x::Vector{Float64}; p::Float64=0.05, n::Int=10, cumulative::Bool=true)::Vector{Float64}`
-"""
+""" ->
 function runquantile(x::Array{Float64}; p::Float64=0.05, n::Int=10, cumulative::Bool=true)::Array{Float64}
     @assert n<size(x,1) && n>1 "Argument n is out of bounds."
     out = zeros(Float64, (size(x,1), size(x,2)))
@@ -310,3 +312,24 @@ function runquantile(x::Array{Float64}; p::Float64=0.05, n::Int=10, cumulative::
     end
     return out
 end
+
+
+@doc doc"""
+Compute the running/rolling autocorrelation of a vector.
+""" ->
+function runacf(x::Array{Float64}; lookback::Int=10, maxlag::Int=7, lags::AbstractArray{Int,1}=0:maxlag)::Matrix{Float64}
+    @assert size(x, 2) == 1 "Autocorrelation input array must be one-dimensional"
+    N = size(x, 1)
+    if length(lags) == 1 && lags[1] == 0
+        return ones((N, 1))
+    end
+    #  @assert all(lags .>= 0) "All autocorrelation lags must be positive integers"
+    #  @assert all(lags .< N+3) "All autocorrelation lags must be less than the number of observations in the input vector"
+    #  @assert all(lags .< lookback+2)
+    @assert lookback < N && lookback > 0
+    out = zeros((N, length(lags))) * NaN
+    @inbounds for i in lookback:N
+        out[i,:] = acf(x[i-lookback+1:i], lags=lags)
+    end
+    return out
+end

test/runtests.jl

@@ -443,6 +443,8 @@ end
     @test tmp.values[10,1] == quantile(x.values[1:10,1], 0.05)
     tmp = runquantile(x, cumulative=false)
     @test tmp.values[10,1] == quantile(x.values[1:10,1], 0.05)
+    tmp = runacf(randn(100))
+    @test all(tmp[10:end,1] .== 1.0)
     # moving average functions
     tmp = sma(x)
     @test size(tmp, 1) == N