import Pkg
Pkg.add(["LaTeXStrings"])

using Plots 
using CSV 
using DataFrames 
using LaTeXStrings 
us_500 = DataFrame(CSV.File("HistoricalPricesUS500.csv")) 
eur_600 = DataFrame(CSV.File("HistoricalPricesSTOXX600.csv")) 
jp_225 = DataFrame(CSV.File("HistoricalPricesJP225.csv")) 

function col_name_format!(df_array) 
    for df in df_array 
        try select!(df, :Date, " Close" => :Close) 
        catch 
        end 
    end 
end 

function format_date_string!(df) 
    index_99 = 1 
    try 
        while df.Date[index_99][7] != '9' 
            index_99 += 1 
        end 
        println(index_99) 
        @inbounds for row in 1:index_99-1 
            df.Date[row] = df.Date[row][1:6] * "20" * df.Date[row][7:8] 
        end 
        @inbounds for row in index_99:size(df, 1) 
            df.Date[row] = df.Date[row][1:6] * "19" * df.Date[row][7:8] 
        end 
    catch BoundsError 
        println("You have already formatted this dataframe") 
    end 
end 

col_name_format!([eur_600, jp_225, us_500]) 
format_date_string!(eur_600) 
format_date_string!(jp_225) 
format_date_string!(us_500) 
CSV.write("HistoricalPricesJP225-formatted.csv", jp_225) 
CSV.write("HistoricalPricesSTOXX600-formatted.csv", eur_600) 
CSV.write("HistoricalPricesUS500-formatted.csv", us_500)

us_500 = DataFrame(CSV.File("HistoricalPricesUS500-formatted.csv", dateformat = "mm/dd/yy"))
eur_600 = DataFrame(CSV.File("HistoricalPricesSTOXX600-formatted.csv", dateformat = "mm/dd/yy"))
jp_225 = DataFrame(CSV.File("HistoricalPricesJP225-formatted.csv", dateformat = "mm/dd/yy"))
sort!.([jp_225, us_500, eur_600], :Date)
mean = x -> sum(x)/length(x)
standard_dev = x -> sqrt((sum(x.^2) + length(x)*mean(x)^2)/(length(x) - 1)) 
function add_log_changes!(df) 
    df.Change = append!([0.0], [log(df.Close[i]/df.Close[i-1]) for i in 2:size(df, 1)]) 
end 
function add_vol_ma!(df, window::Int64) 
    df."VolatilityMA_" = append!(fill(0.0, window + 1), [standard_dev(df.Change[(i - window):i])*sqrt(252) for i in (window + 2):length(df.Change)]) 
    select!(df, Not(:VolatilityMA_), :VolatilityMA_ => "VolatilityMA_$window") 
end 
function plot_volatilities(df, title) 
    return plot(df.Date , [df.VolatilityMA_20 df.VolatilityMA_60 df.constant_vol], label = [L"σ \ ∼ \ MA, T = 20" L"σ \ ∼ \ MA, T = 60" L"σ = Constant \ vol"], title = title) 
end 

add_log_changes!.([jp_225, us_500, eur_600]) 
add_vol_ma!.([jp_225, us_500, eur_600], 20) 
add_vol_ma!.([jp_225, us_500, eur_600], 60) 
us_500.constant_vol = standard_dev(us_500.Change)*sqrt(252) 
jp_225.constant_vol = standard_dev(jp_225.Change)*sqrt(252) 
eur_600.constant_vol = standard_dev(eur_600.Change)*sqrt(252) 
plot() 
display(plot_volatilities(us_500, "S&P500 Volatility over time"))
display(plot_volatilities(eur_600, "STOXX600 Volatility over time"))
display(plot_volatilities(jp_225, "NIKKEI225 Volatility over time"))

function ewma_λ_094!(df) 
    std_t = 0.0 
    for i in 1:99 
        std_t += (0.94^(i - 1))*(df.Change[(100 - i)])^2 
    end 
    std_t = sqrt(252 * std_t * (1-0.94)) 
    df.ewma_1 = Array{Float64, 1}(undef, size(df, 1)) 
    df.ewma_1[1:100] = append!(fill(0.0, 99), [std_t]) 
    df.ewma_1[101:end] = [sqrt( (0.94*(df.Change[i]^2) + 0.06*((df.ewma_1[i - 1])^2)) * sqrt(252) ) for i in 101:size(df, 1)] 
    return df
end

ewma_λ_094!(us_500)
println("ewma_1, row 4 should be: ",sqrt( (0.94*(us_500.Change[102]^2) + 0.06*(us_500.ewma_1[101]^2)) *sqrt(252))) 
us_500[99:154, :ewma_1]

function ewma_094(df)
    nrows = nrow(df)
    df.ewma_1 = zeros(nrows)
    df.ewma_1[100] = sqrt( 252 * sum( 0.94^(i-1) * df.Change[(100-i)]^2 for i in 1:99 ) * (1-0.94))
    for ridx in 101:nrows
        df.ewma_1[ridx] = sqrt( (0.94*(df.Change[ridx]^2) + 0.06*((df.ewma_1[ridx - 1])^2)) * sqrt(252) )
    end
    return df
end

us_500 = ewma_094(us_500)
us_500.ewma_1[99:110]