### A Pluto.jl notebook ###
# v0.17.3

using Markdown
using InteractiveUtils

# ╔═╡ 2f67f6bf-832e-4bb7-bdda-576ea37e98c8
begin
	# setup: we need to specify the paths where we can find our project resources
    _PATH_TO_ROOT = pwd()
    _PATH_TO_SRC = joinpath(_PATH_TO_ROOT, "src")
    _PATH_TO_DATA = joinpath(_PATH_TO_ROOT, "data")
    _PATH_TO_CONFIG = joinpath(_PATH_TO_ROOT, "configuration")

    # what packages are we going to use?
    using DataFrames
    using CSV
    using HTTP
    using TOML
    using Dates
    using StatsPlots
    using Statistics
    using Distributions
    using PlutoUI
	using HypothesisTests
	using PrettyTables
	using Colors
	using Optim
	using Convex
	using SCS
	using MathOptInterface
	
    # alias the AlphaVantage URL -
    DATASTORE_URL_STRING = "https://www.alphavantage.co/query?"

    # What we have here is a classic good news, bad news situation ...
    # Bad news: We don't check in our AlphaVantage API key to GitHub (sorry). 
    # Good news: AlphaVantage API keys are free. 
    # check out: https://www.alphavantage.co/support/#api-key
    configuration_dictionary = TOML.parsefile(joinpath(_PATH_TO_CONFIG, "Configuration.toml"))

    # get some stuff from the configuration dictionary -
    ALPHAVANTAGE_API_KEY = configuration_dictionary["API"]["alphavantage_api_key"]

	# List of colors -
	WHITE = RGB(1.0, 1.0, 1.0)
	BACKGROUND = RGB(0.99, 0.98, 0.96)
	BLUE = RGB(68 / 255, 119 / 255, 170 / 255)
	LBLUE = RGB(102 / 255, 204 / 255, 238 / 255)
	GRAY = RGB(187 / 255, 187 / 255, 187 / 255)
	RED = RGB(238 / 255, 102 / 255, 119 / 255)
    
    # show -
    nothing
end

# ╔═╡ a0e130f0-fe63-4c2b-9250-3a22b1dd6be4
md"""
## Classical Portfolio Management: Markowitz Mean-Variance Portfolio Theory 
"""

# ╔═╡ 530772fd-e03e-466f-a095-74e3ca89cef8
md"""
### Introduction

Portfolio allocation is a classic problem in mathematical finance. Suppose we have a basket of $i=1,2,\dots,\mathcal{P}$ risky assets e.g., individual stocks or exchange-traded funds (ETFs) and we must estimate the relative proportion $\omega_{i}$ of each asset in the basket so that we achieve some objective, for example, a specified minimum growth rate (return).  

The [Markowitz Mean-Variance Portfolio Theory](https://en.wikipedia.org/wiki/Modern_portfolio_theory) is the classical approach for computing the portfolio asset allocations $\omega_{i},i=1,2,\dots\mathcal{P}$ first published in 1952:

* [Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974](https://www.jstor.org/stable/2975974)

Mean-variance portfolio theory assumes that investors are rational and risk-averse. Thus, when given two portfolios that offer the same expected return, a _rational investor_ prefers the _less risky_ portfolio. Further, an investor will take on increased risk only if they are compensated by higher expected returns. On the other hand, investors who want higher expected returns must assume more risk to achieve these desired returns. Individual investors evaluate the trade-off between risk and reward differently based upon their own risk aversion characteristics. 
"""

# ╔═╡ 68a3dce4-2a89-4cf4-ae3d-0230abe179fb
md"""
### Materials and Methods
"""

# ╔═╡ c0b2f1ac-90a6-4d5a-ba55-2e679a1a982f


# ╔═╡ ee1a82c2-a485-4e9a-8b08-323f174bedd5
md"""
### Results and Discussion
"""

# ╔═╡ 9bc330e1-0353-44f2-967a-2f825fb11eae
ticker_symbol_array = [
	"SPY", "SPYD", "SPYG", "SPYV", "SPYX", "VOO", "VTI", "VEA", "VWO", "VNQ", "VGK", "AAPL", "ALLY"
];

# ╔═╡ 4c417596-f5c5-445b-9806-00ea4fbc4c9c
# How many tickers do we have?
𝒫 = length(ticker_symbol_array);

# ╔═╡ 96fb3275-7be3-4611-96ab-46b805477ad4
# what is my budget?
budget_total = 2750.0; # USD

# ╔═╡ 57e56bcf-59a1-4ef9-9394-636d4ecaac96


# ╔═╡ 7cc32583-0ece-4319-b0b1-33583ceae14b
md"""
### Conclusions
"""

# ╔═╡ e7ef9b22-aee2-442d-9021-218e10cf9e30
function ingredients(path::String)
	
	# this is from the Julia source code (evalfile in base/loading.jl)
	# but with the modification that it returns the module instead of the last object
	name = Symbol("Serenity")
	m = Module(name)
	Core.eval(m,
        Expr(:toplevel,
             :(eval(x) = $(Expr(:core, :eval))($name, x)),
             :(include(x) = $(Expr(:top, :include))($name, x)),
             :(include(mapexpr::Function, x) = $(Expr(:top, :include))(mapexpr, $name, x)),
             :(include($path))))
	m
end

# ╔═╡ d8309e86-d613-4987-a87b-0314a7f4ddad
Serenity = ingredients(joinpath(_PATH_TO_SRC, "Include.jl"));

# ╔═╡ 49f6a628-a7cf-4f56-883f-0715f569abca
function download_ticker_data(ticker_symbol_array::Array{String,1})::Dict{String,DataFrame}

    # initialize some storage -
    price_data_dictionary = Dict{String,DataFrame}() # Dict key => ticker symbol and value = price DataFrame

    # setup: specify the query parameters for the call to Alphavantage -
    # check out the API documentation: https://www.alphavantage.co/documentation/
    query_parameters = Dict{String,String}()
    query_parameters["function"] = "TIME_SERIES_DAILY_ADJUSTED" # get the daily close price adjusted
    query_parameters["apikey"] = ALPHAVANTAGE_API_KEY # your alphavantage API key goes here (config block below)
    query_parameters["datatype"] = "csv" # download in CSV (the other option is JSON) format
    query_parameters["outputsize"] = "full" # compact -vs- full: last 100 trading days -vs- up to 20 years of data

    # data type: sub dir where we save the data
    data_type_flag = "daily"
    training_prediction_flag = "training"

    # main download loop -
    for ticker_symbol in ticker_symbol_array

        # check -> have we downloaded this data already?
        data_file_path = joinpath(_PATH_TO_DATA, "$(training_prediction_flag)", "$(data_type_flag)",
            "$(ticker_symbol).csv")
        if (ispath(data_file_path) == false || Serenity.does_data_file_exist(data_file_path) == false)

            # let use know what is going on ...
            with_terminal() do
                println("Starting $(ticker_symbol) download ...")
            end

            # We do NOT have this data file -> download from Alphavantage.co
            # execute API call -> check to see if error -> turn into DataFrame
            query_parameters["symbol"] = ticker_symbol
            url_string = Serenity.build_url_query_string(DATASTORE_URL_STRING, query_parameters)
            data_table = Serenity.http_get_call_with_url(url_string) |> Serenity.check |> 	
				Serenity.process_csv_api_data

            # sleep -
            sleep(10) # to avoid issue w/API limits

            # make the dir to save the data (if we don't have it already) -
            mkpath(joinpath(_PATH_TO_DATA, "$(training_prediction_flag)", "$(data_type_flag)"))

            # dump data table to disk -
            CSV.write(data_file_path, data_table)

            # put the price DataFrame into the price_data_dictionary -
            price_data_dictionary[ticker_symbol] = data_table

        else

            # we already have this ticker downloaded. load the existing file from disk -
            data_table = CSV.read(data_file_path, DataFrame)

            # put the price DataFrame into the price_data_dictionary -
            price_data_dictionary[ticker_symbol] = data_table
        end
    end

    # show -
    return price_data_dictionary
end

# ╔═╡ 4975330e-565f-4419-9aa4-33bbd2ebdcbc
# load the historical price data from disk (if not saved, then download new data)
historical_price_dictionary = download_ticker_data(ticker_symbol_array);

# ╔═╡ 31740764-d1e0-4228-a045-ebf785987e71
historical_return_dictionary = Serenity.compute_fractional_return_array(ticker_symbol_array, 
	historical_price_dictionary, :timestamp=>:adjusted_close, multiplier=100.0);

# ╔═╡ 0b85225d-e6f1-4f68-94ac-76e8a79a1a02
begin

	# what date range are we interested in?
	start = Date(2020,12,17);
	stop = Date(2021,12,17);

	# compute the average return and Σ -
	(μ_bar, Σ) = Serenity.compute_average_fractional_return_and_covariance(ticker_symbol_array, historical_return_dictionary, 
		start, stop);

	# show -
	nothing
end

# ╔═╡ b11ff814-ecde-4a5c-b185-a8abd87a49fc
# compute the minvar portfolio -
result = Serenity.compute_minvar_portfolio_allocation(μ_bar,Σ, 0.01; w_lower=0.0,w_upper=1.0);

# ╔═╡ 99055948-b794-4f05-a31d-b3a2875ac857
ω = max.(0.0,result[2])

# ╔═╡ 1204dfc0-5e46-4c72-bafe-f126c9a2365f
expected_return = (1+(result[4]/100))^(252) - 1

# ╔═╡ 6b7af0b9-65a4-4dc3-a4a4-ef165dcb3959
md"""
__Table XX__: Markowitz allocation table for 𝒫 = $(𝒫) assets in the ticker array (a mixture of different ETFs).
This allocation has variance = $(round(result[3], sigdigits=4)) with a projected daily return = $(round(result[4], sigdigits=2))% (or an $(round(expected_return*100,sigdigits=4))% average return per year).
"""

# ╔═╡ fdc668ac-ece6-4edb-aba8-77a2a51e6817
with_terminal() do

	# make a table with the allocation -
	optimal_flag = result[1] # type MathOptInterface
	w = result[2]

	if (optimal_flag == MathOptInterface.OPTIMAL)
		
		state_table = Array{Any,2}(undef, 𝒫, 3)
		for (index, ticker) ∈ enumerate(ticker_symbol_array)
			state_table[index,1] = ticker
			state_table[index,2] = abs(w[index]) < 1e-4 ? 0.0 : round(w[index],sigdigits=2)
			state_table[index,3] = budget_total*state_table[index,2]
		end

		state_table_header = (
			["ticker","wᵢ","USD"],["","","USD"]
		)

		pretty_table(state_table, header=state_table_header)
	else 
		println("No optimal solution found. Solver returned: $(optimal_flag)")
	end
end

# ╔═╡ aea955b7-dd61-4056-8a24-bf973b841c00
# compute the cybere
u_variable = Serenity.compute_cybernetic_portfolio_allocation(μ_bar, Σ);

# ╔═╡ 832354e4-52f8-4ba2-9086-80da857b82c3
with_terminal() do
	state_table = Array{Any,2}(undef, 𝒫, 3)
	for (index, ticker) ∈ enumerate(ticker_symbol_array)
		state_table[index,1] = ticker
		state_table[index,2] = abs(u_variable[index]) < 1e-4 ? 0.0 : round(u_variable[index],sigdigits=2)
		state_table[index,3] = budget_total*state_table[index,2]
	end

	state_table_header = (
		["ticker","uᵢ","USD"],["","","USD"]
	)

	pretty_table(state_table, header=state_table_header)
end

# ╔═╡ 1b43142b-de39-43d4-aeea-e9fd4a9f7a6c
WA_cybernetic = Serenity.simulate_insample_portfolio_allocation(ticker_symbol_array,historical_return_dictionary, u_variable, 
	budget_total, Date(2021,1,17), stop; multiplier=(1.0/100.0));

# ╔═╡ 8cc221da-cb6b-4897-97a5-5376df98342e
WA_markowitz = Serenity.simulate_insample_portfolio_allocation(ticker_symbol_array,historical_return_dictionary, ω, 
	budget_total, Date(2021,1,17), stop; multiplier=(1.0/100.0));

# ╔═╡ 3459367f-24e1-46b0-89b6-5165d5ebc00d
begin
	plot(sum(WA_cybernetic, dims=2), legend=:topleft, c=RED, lw=2, label="Cybernetic")
	plot!(sum(WA_markowitz,dims=2),lw=2,c=BLUE, label="Markowitz")
	xlabel!("Time step index (AU)", fontsize=18)
	ylabel!("Wealth (USD)", fontsize=18)
end

# ╔═╡ c3647093-c7d8-4ce6-95f3-85d2517b4bfd
begin

	# monthly reallocation -
	number_of_test_months = 12
	wealth_array = Array{Array{Float64,2},1}()
	b = 2750.0
	UA = Array{Float64,2}(undef, number_of_test_months-1, 𝒫)
	base_month = Date(2020,12,17);
	
	for month_index ∈ 2:number_of_test_months

		if (month_index > 2)
			W = last(wealth_array)
			b = last(sum(W,dims=2))
		end

		# what is the next month -
		previous_month = Date(2021,(month_index - 1),17);
		next_month = Date(2021,month_index,17);

		# compute the average return and cov for base -> next_month -
		(μ_bar_month, Σ_month) = Serenity.compute_average_fractional_return_and_covariance(ticker_symbol_array,
			historical_return_dictionary, base_month, next_month);

		# compute u-var -
		u_variable_month = Serenity.compute_cybernetic_portfolio_allocation(μ_bar_month, Σ_month);

		# update UA -
		for (i,u) ∈ enumerate(u_variable_month)
			UA[month_index-1,i] = u
		end

		# compute cybernetic allocation -
		WA_cybernetic_month = Serenity.simulate_insample_portfolio_allocation(ticker_symbol_array,historical_return_dictionary,
				u_variable_month, b, previous_month, next_month; multiplier=(1.0/100.0));

		# grab this month -
		push!(wealth_array,WA_cybernetic_month);
	end
end

# ╔═╡ 4ae85196-b25a-4dbf-b4ff-1c910d7ea392
with_terminal() do

	(nr,nc) = size(UA)
	state_table = Array{Any,2}(undef,𝒫, nr+1)
	for (index,ticker) ∈ enumerate(ticker_symbol_array)
		state_table[index,1] = ticker

		for tmp = 1:nr
			state_table[index,tmp + 1] = UA[tmp,index] 
		end
	end

	pretty_table(state_table)
end

# ╔═╡ 3f488680-fd2b-4d3d-9eac-e64ff6d725c7
WC = vcat(wealth_array...)

# ╔═╡ 79e92731-b9e5-4518-a09a-2b0181a37359
begin

	# monthly reallocation -
	wealth_array_m = Array{Array{Float64,2},1}()
	total_row_count_m = 0
	b_m = 2750.0
	
	for month_index ∈ 2:number_of_test_months

		if (month_index > 2)
			W = last(wealth_array_m)
			b_m = last(sum(W,dims=2))
		end

		# what is the next month -
		previous_month = Date(2021,(month_index - 1),17);
		next_month = Date(2021,month_index,17);

		# compute the average return and cov for base -> next_month -
		(μ_bar_month, Σ_month) = Serenity.compute_average_fractional_return_and_covariance(ticker_symbol_array,
			historical_return_dictionary, base_month, next_month);

		# compute u-var -
		result = Serenity.compute_minvar_portfolio_allocation(μ_bar_month,Σ_month, 0.04; w_lower=0.0,w_upper=1.0);
		ω_variable_month = max.(0.0,result[2])

		# compute cybernetic allocation -
		WA_markowitz_m = Serenity.simulate_insample_portfolio_allocation(ticker_symbol_array,historical_return_dictionary, 
			ω_variable_month, b_m, previous_month, next_month; multiplier=(1.0/100.0));

		# grab this month -
		push!(wealth_array_m,WA_markowitz_m);
	end
end

# ╔═╡ 338c1f4d-a85c-4bf2-8bc0-f099fa345824
WM = vcat(wealth_array_m...)

# ╔═╡ 87913c8b-4cf5-4f92-9992-d5cc94cd138d
begin
	plot(sum(WA_cybernetic, dims=2), legend=:topleft, c=GRAY, label="Cybernetic (annual)")
	plot!(sum(WA_markowitz, dims=2), legend=:topleft, c=LBLUE, label="Markowitz (annual)")
	plot!(sum(WC,dims=2), c=RED, label="Cybernetic", lw=2)
	plot!(sum(WM,dims=2),lw=2,c=BLUE, label="Markowitz")
end

# ╔═╡ 1d6818a0-5ffe-11ec-393e-5bcad6dcfdab
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deps = ["Artifacts", "JLLWrappers", "Libdl", "OpenBLAS_jll", "Pkg"]
git-tree-sha1 = "a96402e3b494a8bbec61b1adb86d4be04112c646"
uuid = "af6e375f-46ec-5fa0-b791-491b0dfa44a4"
version = "2.1.4+0"

[[deps.SCS_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "OpenBLAS_jll", "Pkg"]
git-tree-sha1 = "6cdaccb5e6a69455f960de1ae445ba1de5db9d0d"
uuid = "f4f2fc5b-1d94-523c-97ea-2ab488bedf4b"
version = "2.1.2+1"

[[deps.SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"

[[deps.Scratch]]
deps = ["Dates"]
git-tree-sha1 = "0b4b7f1393cff97c33891da2a0bf69c6ed241fda"
uuid = "6c6a2e73-6563-6170-7368-637461726353"
version = "1.1.0"

[[deps.SentinelArrays]]
deps = ["Dates", "Random"]
git-tree-sha1 = "244586bc07462d22aed0113af9c731f2a518c93e"
uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c"
version = "1.3.10"

[[deps.Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"

[[deps.Setfield]]
deps = ["ConstructionBase", "Future", "MacroTools", "Requires"]
git-tree-sha1 = "0afd9e6c623e379f593da01f20590bacc26d1d14"
uuid = "efcf1570-3423-57d1-acb7-fd33fddbac46"
version = "0.8.1"

[[deps.SharedArrays]]
deps = ["Distributed", "Mmap", "Random", "Serialization"]
uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"

[[deps.Showoff]]
deps = ["Dates", "Grisu"]
git-tree-sha1 = "91eddf657aca81df9ae6ceb20b959ae5653ad1de"
uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f"
version = "1.0.3"

[[deps.Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"

[[deps.SortingAlgorithms]]
deps = ["DataStructures"]
git-tree-sha1 = "b3363d7460f7d098ca0912c69b082f75625d7508"
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
version = "1.0.1"

[[deps.SparseArrays]]
deps = ["LinearAlgebra", "Random"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

[[deps.SpecialFunctions]]
deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
git-tree-sha1 = "e08890d19787ec25029113e88c34ec20cac1c91e"
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
version = "2.0.0"

[[deps.Static]]
deps = ["IfElse"]
git-tree-sha1 = "7f5a513baec6f122401abfc8e9c074fdac54f6c1"
uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
version = "0.4.1"

[[deps.StaticArrays]]
deps = ["LinearAlgebra", "Random", "Statistics"]
git-tree-sha1 = "3c76dde64d03699e074ac02eb2e8ba8254d428da"
uuid = "90137ffa-7385-5640-81b9-e52037218182"
version = "1.2.13"

[[deps.Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

[[deps.StatsAPI]]
git-tree-sha1 = "0f2aa8e32d511f758a2ce49208181f7733a0936a"
uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
version = "1.1.0"

[[deps.StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
git-tree-sha1 = "2bb0cb32026a66037360606510fca5984ccc6b75"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.33.13"

[[deps.StatsFuns]]
deps = ["ChainRulesCore", "InverseFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
git-tree-sha1 = "bedb3e17cc1d94ce0e6e66d3afa47157978ba404"
uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
version = "0.9.14"

[[deps.StatsPlots]]
deps = ["Clustering", "DataStructures", "DataValues", "Distributions", "Interpolations", "KernelDensity", "LinearAlgebra", "MultivariateStats", "Observables", "Plots", "RecipesBase", "RecipesPipeline", "Reexport", "StatsBase", "TableOperations", "Tables", "Widgets"]
git-tree-sha1 = "d6956cefe3766a8eb5caae9226118bb0ac61c8ac"
uuid = "f3b207a7-027a-5e70-b257-86293d7955fd"
version = "0.14.29"

[[deps.StructArrays]]
deps = ["Adapt", "DataAPI", "StaticArrays", "Tables"]
git-tree-sha1 = "2ce41e0d042c60ecd131e9fb7154a3bfadbf50d3"
uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
version = "0.6.3"

[[deps.SuiteSparse]]
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"

[[deps.TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"

[[deps.TableOperations]]
deps = ["SentinelArrays", "Tables", "Test"]
git-tree-sha1 = "e383c87cf2a1dc41fa30c093b2a19877c83e1bc1"
uuid = "ab02a1b2-a7df-11e8-156e-fb1833f50b87"
version = "1.2.0"

[[deps.TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"

[[deps.Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
git-tree-sha1 = "bb1064c9a84c52e277f1096cf41434b675cd368b"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.6.1"

[[deps.Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"

[[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[deps.TranscodingStreams]]
deps = ["Random", "Test"]
git-tree-sha1 = "216b95ea110b5972db65aa90f88d8d89dcb8851c"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.9.6"

[[deps.URIs]]
git-tree-sha1 = "97bbe755a53fe859669cd907f2d96aee8d2c1355"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.3.0"

[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.UnicodeFun]]
deps = ["REPL"]
git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
version = "0.4.1"

[[deps.Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "3e61f0b86f90dacb0bc0e73a0c5a83f6a8636e23"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.19.0+0"

[[deps.Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "66d72dc6fcc86352f01676e8f0f698562e60510f"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.23.0+0"

[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.1"

[[deps.Widgets]]
deps = ["Colors", "Dates", "Observables", "OrderedCollections"]
git-tree-sha1 = "80661f59d28714632132c73779f8becc19a113f2"
uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62"
version = "0.6.4"

[[deps.WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.5"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[deps.Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[deps.Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"

[[deps.Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"

[[deps.Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"

[[deps.Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[deps.Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"

[[deps.Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[deps.Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[deps.Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"

[[deps.Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"

[[deps.xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""

# ╔═╡ Cell order:
# ╟─a0e130f0-fe63-4c2b-9250-3a22b1dd6be4
# ╟─530772fd-e03e-466f-a095-74e3ca89cef8
# ╟─68a3dce4-2a89-4cf4-ae3d-0230abe179fb
# ╠═c0b2f1ac-90a6-4d5a-ba55-2e679a1a982f
# ╟─ee1a82c2-a485-4e9a-8b08-323f174bedd5
# ╠═9bc330e1-0353-44f2-967a-2f825fb11eae
# ╠═4c417596-f5c5-445b-9806-00ea4fbc4c9c
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