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library(leaflet)
leaflet() %>% setView(lng = 16.36, lat = 48.21, zoom = 10) %>% addTiles()

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#load packages
library(quantmod)
library(rvest)
library(reshape2)
library(plotly)
library(akima)

#get underlying stock info and last trade date
symbol <- "AAPL"
priceInfo <- getQuote(symbol)

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library(plotly)

xaxx <- list(
  gridcolor='rgb(255, 255, 255)',
  zerolinecolor='rgb(255, 255, 255)',
  showbackground=TRUE,
  backgroundcolor='rgb(230, 230,230)',
  title = "Moneyness"
)

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#get coordinates of NAs in grid
toInterpolate <- which(is.na(ivGridCalls))
coords <- cbind(toInterpolate%%dim(ivGridCalls)[1], toInterpolate%/%dim(ivGridCalls)[1] + 1)
coords[coords[,1] == 0, 2] <- coords[coords[,1] == 0, 2] - 1 
coords[coords[,1] == 0, 1] <- dim(ivGridCalls)[1]

#loop through NAs and interpolate
for(i in 1:nrow(coords)){
  #get the coordinates of a 10x10 area around the missing value
  x1 <- max(coords[i,1] - 10, 1)

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divYield <- getQuote(symbol, what = yahooQF("Dividend Yield"))$`Dividend Yield`
if(is.na(divYield)){divYield <- 0}

#calculating IV
calls$iv <- apply(calls, 1, getIV, S = lastPrice, r = 0.0011, q = divYield, type = "call")
puts$iv <- apply(puts, 1, getIV, S = lastPrice, r = 0.0011, q = divYield, type = "put")

#create grids
library(reshape2)
ivGridCalls <- acast(calls, ttm ~ moneyness, value.var = "iv")

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#generalized black scholes merton model
gBSM <- function(S, X, sigma, r, q, ttm, type){
#S = stock price
#X = strike price
#sigma = volatility
#r = risk free interest rate
#q = dividend yield
#ttm = time to maturity in days
#type = option type
  

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#load packages
library(quantmod)
library(rvest)

#get underlying stock info and last trade date
symbol <- "AAPL"
priceInfo <- getQuote(symbol)
lastPrice <- priceInfo$Last
date <- as.Date(priceInfo$`Trade Time`)

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volOptimFun <- function(sigma, price, S, K, r, q, ttm, type){
  abs(price - gBSM(S, K, sigma, r, q, ttm, type))
}

optimize(volOptimFun, interval = c(0, 2), price = price, S = S, K = K, r = r, q = q, ttm = ttm, type = type)

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gBSM <- function(S, X, sigma, r, q, ttm, type){
#S = stock price
#X = strike price
#sigma = volatility
#r = risk free interest rate
#q = dividend yield
#ttm = time to maturity in days
#type = option type

  b <- r - q

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library(plotly)
library(data.table)

data <- mtcars[,c(1,3:7)]
corrdata <- cor(data)


#do this before the transformation!
corrdata[upper.tri(corrdata, diag = TRUE)] <- NA
corrdata <- corrdata[-1, -ncol(corrdata)]