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def toDouble(s: String) = {
  if (s.contains("/")) { 
    val Array(a, b) = s.split("/");
    a.toDouble / b.toDouble
  } else
    s.toDouble
}

val lines = List(
  io.Source.fromFile("odds2014.txt").getLines.toList,

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import org.apache.commons.io.FileUtils
import java.io.File
import java.net.URL
import java.lang.Thread

val xbrlFolder = new File("xbrl")

for (code <- 1301 to 9997) {

  println(code)

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import java.lang.Thread
import java.net.URL
import java.io.File
import org.openqa.selenium.firefox.FirefoxDriver
import org.openqa.selenium.By
import org.apache.commons.io.FileUtils

val mail = "enter your email address"
val password = "enter your password"
val maxArticleId = 1477 // Please manually set maxArticleId

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require(rugarch)

#SP500の日足データを使う。
data(sp500ret)

#まずは通常のGARCH(1,1)。
spec.sg <- ugarchspec(
    variance.model = list(model = "sGARCH", garchOrder=c(1,1)),
    #平均値のモデルにはARIMA(1,1,1)を使う。
    mean.model = list(armaOrder = c(1,1,1), arfima = T),

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roll.eg <- ugarchroll(
    spec.eg, sp500ret,
    #100日毎にパラメータを推定しなおす。
    refit.every = 100, 
    #一日先の値を予測する。
    n.ahead = 1,
    #予測開始地点を指定するにはforecast.lengthかn.startのどちらかを使う。
    n.start = nrow(sp500ret)-500, #5023日目から予測を始める。sp500retには5523日分のデータが入っている。
    refit.window = "moving", solver = "hybrid", keep.coef = TRUE)

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sp500 <- tail(sp500ret,n=500)$SP500RET
sigma <- df.eg[, 'Sigma', drop = FALSE]$Sigma
#トレーニング期間の分散を定数cに設定する
const <- sd(head(sp500ret,n=5000)$SP500RET)^2
leverage <- const / sigma^2
#buy&holdのリターン
prod(1 + sp500)
#OVSのリターン
prod(1 + sp500*leverage)

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#https://github.com/Marigold/universal-portfolios/blob/master/universal/algos/anticor.py
#ここから一部引用

#時刻0からn-1までの全期間のポートフォリオの状態が計算される
def weights(self, X):
    #期待値やラグ付き相関の計算で使うウィンドウのサイズ
    window = self.window
    #全銘柄の株価時系列データ（ただし前日比の相対価格データ）
    port = X
    #nは時系列の長さ。mは銘柄の数。

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function OLPS_cli(dataset) 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This file is part of OLPS: http://OLPS.stevenhoi.org/
% Original authors: Bin LI, Doyen Sahoo, Steven C.H. Hoi
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

cd Strategy;

opts.quiet_mode = 0; opts.display_interval = 245;
opts.log_mode = 1; opts.mat_mode = 1;

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library(randomForest)
library(RSQLite)
 
random.rows <- function(df, n) {
  df[sample(nrow(df),n),]
}
 
fix.cb <- function(df) {
  c1 <- df[df$finishing_position == "TRUE",]
  c2 <- df[df$finishing_position == "FALSE",]

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