addiversitas’s gists

addiversitas / basicChart.R

Last active September 7, 2020 14:10

basic plotly chart in a shiny app

	library("shiny")
	library("plotly")
	library("data.table")

	dates <- seq(as.Date("2019-02-01"),length=12,by="months") - 1
	books <- c(17,3,3,18,20,19,15,12,17,4,18,7)
	laptops <- c(13,5,8,12,27,32,31,9,22,11,12,30)
	tvs <- c(30,41,22,11,4,33,16,17,8,49,14,50)

	data <- data.frame(dates = dates,

addiversitas / finalChart.R

Created September 7, 2020 18:21

end result after improving the plotly output

	library("shiny")
	library("plotly")
	library("data.table")

	dates <- seq(as.Date("2019-02-01"),length=12,by="months") - 1
	books <- c(17,3,3,18,20,19,15,12,17,4,18,7)
	laptops <- c(13,5,8,12,27,32,31,9,22,11,12,30)
	tvs <- c(30,41,22,11,4,33,16,17,8,49,14,50)

	data <- data.frame(dates = dates,

addiversitas / improvedCorrelationPlot.R

Last active October 5, 2020 11:36

full code for the improved correlation plot

	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)]

addiversitas / generalizedBlackScholesMerton.R

Created October 17, 2020 21:39

generalized black scholes merton implementation

	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

addiversitas / solveImpliedVol.R

Created October 17, 2020 21:57

	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)

addiversitas / scrapeOptionChain.R

Last active September 2, 2021 00:40

option chain web scraping example for AAPL

	#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`)

addiversitas / ivCalculation.R

Created October 18, 2020 13:22

iv calculation for APPL calls and puts

	#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

addiversitas / ivCalculationDiv.R

Last active October 18, 2020 13:52

iv calculation for APPL calls and puts including dividend yield

	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")

addiversitas / interpolationCalls.R

Created October 18, 2020 15:46

interpolation example for calls

	#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)

addiversitas / surfacePlot.R

Last active October 18, 2020 16:12

example of implied volatility surface plot

Stefan Haring addiversitas