I want to use the ewma (Exponential Weighted Moving Average) function available in the pandas library in Python with an equivalent Julia function. In Julia, there is a function called MarketTechnicals.ema(m) which calculates the EMA values. However, this function starts calculating values from the mth element in a data series of size n, so the resulting data would be of length (n-m+1). While this is understandable since the span is m, the pandas ewma function calculates this from the very first element, no matter what the span length is, as shown below:
#python
import pandas as pd
data = {"val": [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
df['val'].ewm(m).mean()
#output m=3
0 1.000000
1 1.571429
2 2.189189
3 2.851429
4 3.555698
5 4.299079
This Python code returns n values if the length of df['val'] is n. But following Julia snippet would be of length (n-m+1)
# julia
using DataFrames, MarketTechnicals
df = DataFrame(val = [1, 2, 3, 4, 5, 6])
ema(df.val,m)
#output m=3
4×1 Matrix{Float64}:
2.0
3.0
4.0
5.0
I came across a repo that allows using pandas functionality in Julia, but I am not clear with the syntax and couldn't find any examples online.
Can someone help me with a Julia equivalent for the pandas ewma function?
Using the pd.ewma explanation from the pandas source code, I created following function for julia. It caters options of span, com, halflife, adjust, ignore parameters
# julia code to recreate pandas.ewma
abstract type AlphaCalc end
struct Com{T<:Float64}<:AlphaCalc
com::T
end
struct Span{T<:Float64}<:AlphaCalc
span::T
end
struct Halflife{T<:Float64}<:AlphaCalc
halflife::T
end
calcAlpha(val::Com) = 1/(1 + val.com)
calcAlpha(val::Span) = 2/(1 + val.span)
calcAlpha(val::Halflife) = 1 - exp(log(0.5)/val.halflife)
# julia code to recreate pandas.ewma
function ewma(
data::AbstractVector,
alphaMethod::AlphaCalc;
adjust::Bool=true,
ignore_na::Bool=false
)
alpha = calcAlpha(alphaMethod)
n_samples = length(data)
ewma_result = Vector{Float64}(undef, n_samples)
if ignore_na
ewma_result[1] = first(data)
weights = [1.]
for i in 2:n_samples
if isnan(data[i])
ewma_result[i] = ewma_result[i-1]
else
if !isnan(data[i-1])
weights .*= (1 - alpha)
push!(weights, 1)
end
# When adjust is True, use the weighted sum divided by the sum of weights
if adjust
weighted_sum = sum([weight * value for (weight, value) in zip(weights, data[1:i][.!isnan.(data[1:i])])])
ewma_result[i] = weighted_sum / sum(weights)
# When adjust is False, use the recursive formula
else
ewma_result[i] = (1 - alpha) * ewma_result[i-1] + alpha * data[i]
end
end
end
else
ewma_result[1] = first(data)
for i in 2:n_samples
# When adjust is True, use the weighted sum divided by the sum of weights
if adjust
weights = [(1 - alpha) ^ (i - j - 1) for j in 0:(i-1)]
weighted_sum = sum([weight * value for (weight, value) in zip(weights, data[1:i])])
ewma_result[i] = weighted_sum / sum(weights)
# When adjust is False, use the recursive formula
else
ewma_result[i] = (1 - alpha) * ewma_result[i-1] + alpha * data[i]
end
end
end
return ewma_result
end
It was tested in julia as-
data = [1., 2., 3., 4., 5., 6., 8.0]
ewma_result = ewma(data, Com(5.), adjust=false, ignore_na=false);
println("EWMA result: ", ewma_result)
ewma_result = ewma(data, Span(5.), adjust=false, ignore_na=true);
println("EWMA result: ", ewma_result)
ewma_result = ewma(data, Halflife(5.), adjust=true, ignore_na=true);
println("EWMA result: ", ewma_result)
#outputs
EWMA result: [1.0, 1.1666666666666667, 1.4722222222222223, 1.8935185185185186, 2.4112654320987654, 3.0093878600823047, 3.841156550068587]
EWMA result: [1.0, 1.3333333333333335, 1.888888888888889, 2.5925925925925926, 3.3950617283950617, 4.263374485596708, 5.508916323731139]
EWMA result: [1.0, 1.5346019613807635, 2.0921248294619423, 2.672350105842331, 3.2749760411274242, 3.8996216516650244, 4.754261118819774]
with corresponding python functionality as-
>>> import pandas as pd
>>> data = [1., 2., 3., 4., 5., 6., 8.0]
>>> data = pd.Series(data)
>>> data.ewm(com=5.0, adjust=False, ignore_na=False).mean()
0 1.000000
1 1.166667
2 1.472222
3 1.893519
4 2.411265
5 3.009388
6 3.841157
dtype: float64
>>> data.ewm(span=5.0, adjust=False, ignore_na=True).mean()
0 1.000000
1 1.333333
2 1.888889
3 2.592593
4 3.395062
5 4.263374
6 5.508916
dtype: float64
>>> data.ewm(halflife=5.0, adjust=True, ignore_na=True).mean()
0 1.000000
1 1.534602
2 2.092125
3 2.672350
4 3.274976
5 3.899622
6 4.754261
dtype: float64