DataFrame join rename multiple columns in join command

So i want to outerjoin three dataframes and rename their columns at the same time.

Here is the code that I try to do

using DataFramesMeta, Dates, Plots, DataFrames, MarketData, PyCall

start = DateTime(2018,1,1)
IBCI = DataFrame(yahoo(Symbol("IBCI.DE"), YahooOpt(period1 = start)))
IUSA =  DataFrame(yahoo(Symbol("IUSA.DE"), YahooOpt(period1 = start)))
IEMA =  DataFrame(yahoo(Symbol("IEMA.AS"), YahooOpt(period1 = start)))

dollar_portfolio = outerjoin(IBCI[:,[:timestamp, :Open]],
                             IUSA[:,[:timestamp, :Open]],
                             IEMA[:,[:timestamp, :Open]],
                             on=:timestamp, makeunique=true, 
                             renamecols= "IBCI" => "IUSA"=> "IEMA")

This give me the following error

ERROR: LoadError: MethodError: no method matching outerjoin(::DataFrame, ::DataFrame, ::DataFrame; on=:timestamp, makeunique=true, renamecols="IBCI" => ("IUSA" => "IEMA"))
Closest candidates are:
  outerjoin(::AbstractDataFrame, ::AbstractDataFrame, ::AbstractDataFrame...; on, makeunique, validate, matchmissing) at C:\Users\ignac\.julia\packages\DataFrames\BM4OQ\src\join\composer.jl:1044 got unsupported keyword argument "renamecols"
  outerjoin(::AbstractDataFrame, ::AbstractDataFrame; on, makeunique, source, indicator, validate, renamecols, matchmissing) at C:\Users\ignac\.julia\packages\DataFrames\BM4OQ\src\join\composer.jl:1013
 [1] kwerr(::NamedTuple{(:on, :makeunique, :renamecols), Tuple{Symbol, Bool, Pair{String, Pair{String, String}}}}, ::Function, ::DataFrame, ::DataFrame, ::DataFrame)
   @ Base .\error.jl:163
 [2] top-level scope

If I only join two of them everything works fine. But I would like to join multiple at the same time. Does anyone knows how I should do this?
Thanks in advance

column renaming is only supported if you do outerjoin for two data frames. If you have more than two and want column renaming then either:

  1. rename columns before the operations
  2. do sequential outerjoin of 2 data frames at a time
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