Noise Test - Adjust price up to a % of the bars true range

Noise_Test.jl

"""
```
tr(hlc::Matrix{T})::Array{Float64}
```
True range
"""
function tr(hlc::AbstractMatrix{T})::Array{Float64} where {T<:Real}
    @assert size(hlc,2) == 3 "HLC array must have 3 columns."
    n = size(hlc,1)
    out = zeros(n)
    out[1] = NaN
    @inbounds for i=2:n
        out[i] = max(hlc[i,1]-hlc[i,2], hlc[i,1]-hlc[i-1,3], hlc[i-1,3]-hlc[i,2])
    end
    return out[:,1]
end

function noise_perc(x::Array{Float64}; perc::Float64=.4)
    noise_out = fill(0.0,size(t_r,1))
    for i =1:size(t_r,1)
        if isnan(t_r[i]) == 0
          percs = (perc* (t_r[i]))
          pos_range = collect(0.0:.25:percs)
          neg_range = -(pos_range)
          all_range = vcat(pos_range,neg_range)
          noise_out[i] = (x[i]) + sample(all_range)  # sample() to choose a random number within the range up to perc= maximum of TR
        else
            noise_out[i] = 0
        end
    end
  return noise_out
end

noise_test = true

# Build H L C matrix
    if noise_test == false
        # OHLC
        o = Float64.(data.Open)
        h = Float64.(data.High)
        l = Float64.(data.Low)
        c = Float64.(data.Close)
        c_sma200 = runmean(c; n=200, cumulative=false)
    elseif noise_test == true
        # OHLC
        o = Float64.(data.Open)
        h = Float64.(data.High)
        l = Float64.(data.Low)
        c = Float64.(data.Close)
        c_sma200 = runmean(c; n=200, cumulative=false)
        m = Array{Float64}(zeros(size(data,1),0))
        m = hcat(m,h)
        m = hcat(m,l)
        m = hcat(m,c)

        # True Range
        t_r = tr(m)

        o = noise_perc(o,perc=.4)
        h = noise_perc(h,perc=.4)
        l = noise_perc(l,perc=.4)
        c = noise_perc(c,perc=.4)
    end

    plot(c[2000:2500])
    plot!(data.Close[2000:2500])

# Run function
# perc= sets the maximum % to adjust the prices by
noise_perc(data1_c,perc=.4)