Gamma

Gamma Exposure

Credit given to Toby Moran for gamma exposure calculations and chart development techniques for SPX

I added my own charts for AAPL and SOFI, and LCID. Since I have an interest in exploring trends within midcap stocks, I chose SOFI and LCID based on revenue and past earnings report.

Future Plans for the Project: - updating spot price for accuracy - pulling live data to report GEX charts in real-time - calculating gamma exposure based on expiration date

Looking for total put and call gamma at each strike

total gamma at each strike

Libraries that I will be using

library(data.table)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:data.table':
## 
##     hour, isoweek, mday, minute, month, quarter, second, wday, week,
##     yday, year

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(timeDate)
library(stringr)
library(formattable)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:data.table':
## 
##     between, first, last

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:formattable':
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##     style

## The following object is masked from 'package:stats':
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##     filter

## The following object is masked from 'package:graphics':
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##     layout

library(tidyr)
library(purrr)

## 
## Attaching package: 'purrr'

## The following object is masked from 'package:data.table':
## 
##     transpose

will be using fread which is faster and more convenient than read.table or read.csv

#option chain data
optionChain <-fread("./spx_quotedata-2.csv")
lastSalePrices <- optionChain[, "Last Sale"]
lastSalePrices

##        Last Sale
##     1:   3404.54
##     2:   3362.70
##     3:   3020.46
##     4:      0.00
##     5:      0.00
##    ---          
## 10685:      0.00
## 10686:      0.00
## 10687:      0.00
## 10688:      0.00
## 10689:      0.00

spotLine <- fread("./spx_quotedata-2.csv", skip=3, nrows = 1)
spotLine

##    Expiration Date               Calls Last Sale Net    Bid  Ask Volume IV
## 1: Tue Dec 26 2023 SPXW231226C01200000   3404.54   0 3569.4 3570      0  0
##    Delta Gamma Open Interest Strike                Puts Last Sale Net Bid  Ask
## 1:     1     0             2   1200 SPXW231226P01200000         0   0   0 0.05
##    Volume IV Delta Gamma Open Interest
## 1:      0  0     0     0             0

spotLineData <- colnames(spotLine[,3])
 # strsplit(as.character(colnames(spotLine[,3])), ":")
spotLineData

## [1] "Last Sale"

#obtained spot price for first row
spotPrice <- as.numeric(spotLine[1,3])
  #as.numeric(str_trim(spotLineData[[3]][1]))
spotPrice

## [1] 3404.54

#range of spot prices
fromS = 0.8*spotPrice
toS = 1.2*spotPrice

#Extracting current data of the spot price

dateLine <- fread("./spx_quotedata-2.csv",skip =3, nrows = 1)
dateLine

##    Expiration Date               Calls Last Sale Net    Bid  Ask Volume IV
## 1: Tue Dec 26 2023 SPXW231226C01200000   3404.54   0 3569.4 3570      0  0
##    Delta Gamma Open Interest Strike                Puts Last Sale Net Bid  Ask
## 1:     1     0             2   1200 SPXW231226P01200000         0   0   0 0.05
##    Volume IV Delta Gamma Open Interest
## 1:      0  0     0     0             0

todaysDateData <- strsplit(as.character(dateLine[,1]), " ")
# strsplit(as.character(colnames(dateLine[,1])), " ")
todaysDateData

## [[1]]
## [1] "Tue"  "Dec"  "26"   "2023"

todayYear <- todaysDateData[[1]][4]
todayMonth <- todaysDateData[[1]][2]
todayDay <- todaysDateData[[1]][3]
#using lubridate
todayDate <-as.Date(ymd(paste0(todayYear, '-', todayMonth, '-', todayDay)))
todayDate

## [1] "2023-12-26"

Fixing the option chain column names

colnames(optionChain) <-c('ExpirationDate','Calls','CallLastSale','CallNet','CallBid','CallAsk','CallVol',
'CallIV','CallDelta','CallGamma','CallOpenInt','StrikePrice','Puts','PutLastSale',
'PutNet','PutBid','PutAsk','PutVol','PutIV','PutDelta','PutGamma','PutOpenInt')
#changing the date format
optionChain$ExpirationDate <-as.Date(mdy(substring(optionChain$ExpirationDate,5)))

Will be calculating if the expiration is the third Friday of the month (monthly option expiration)

Note: SPX is for monthly expiration and SPXW is for weekly expiration

#create another column
#need to use str split
expiration <- fread("./spx_quotedata-2.csv",skip =3, nrows = 1)
expiration

##    Expiration Date               Calls Last Sale Net    Bid  Ask Volume IV
## 1: Tue Dec 26 2023 SPXW231226C01200000   3404.54   0 3569.4 3570      0  0
##    Delta Gamma Open Interest Strike                Puts Last Sale Net Bid  Ask
## 1:     1     0             2   1200 SPXW231226P01200000         0   0   0 0.05
##    Volume IV Delta Gamma Open Interest
## 1:      0  0     0     0             0

#expr <- strsplit(as.character(optionChain$Calls), "W")

expr <- sapply(strsplit(as.character(expiration[,2]), "W"), function(x) x[1])
sapply

## function (X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE) 
## {
##     FUN <- match.fun(FUN)
##     answer <- lapply(X = X, FUN = FUN, ...)
##     if (USE.NAMES && is.character(X) && is.null(names(answer))) 
##         names(answer) <- X
##     if (!isFALSE(simplify)) 
##         simplify2array(answer, higher = (simplify == "array"))
##     else answer
## }
## <bytecode: 0x7fb1825e54e8>
## <environment: namespace:base>

expr

## [1] "SPX"

#using grepl and ifelse to determine monthly expiration
optionChain$monthlyExpiration <- ifelse(grepl("W", as.character(expiration[, 2])), FALSE, TRUE)

Calculating Gamma Spot Exposure

Option’s Gamma * Contract Size * Open Interest * Spot Price ^ 2 * 0.01

Call Gamma is positive

Put Gamma is negative

optionChain$CallGEX <- optionChain$CallGamma * 100 * optionChain$CallOpenInt * spotPrice^2 *0.01
optionChain$PutGEX <- optionChain$PutGamma * 100 * optionChain$PutOpenInt * spotPrice^2 *0.01 * -1
optionChain$TotalGamma <- (optionChain$CallGEX + optionChain$PutGEX) / 10^9
#calculate the negative and pos
# strike (gamma * Open Interest * 100) and puts (gamma * Open Interest *-100)
#create a dataframe for plotting gex charts (both put, calls , and total gamma)
#range is +/ 20% of spot price
df <- optionChain %>% 
  filter(StrikePrice > fromS, StrikePrice < toS) %>%
  group_by(StrikePrice) %>% 
  summarise(CallGEX = sum(CallGEX), PutGEX = sum(PutGEX), TotalGamma = sum(TotalGamma))
#plotting variables
strike <- optionChain$StrikePrice
TotalGam <- round(sum(df$TotalGamma),3)

Spot Gamma Exposure Chart

Total Gamma exposure over specified strike price range

spotExpChart <- plot_ly(df,x= ~StrikePrice, y = ~TotalGamma, type = "bar", name = "Total Gamma")
spotExpChart <- spotExpChart %>% add_lines(x=spotPrice, line=list(color= "blue", dash = 'dot'), name= paste('Spot Price: ', round(spotPrice,0)))
spotExpChart <- spotExpChart %>%layout(
  title = paste("Total Gamma: $", TotalGam,  " Bn per 1% SPX Move ",  todayDate),
  xaxis=list(title='Strike Price'),
  yaxis=list(title='Spot Gamma Exposure ($ billions/1% move)')
)
spotExpChart

The graph above depicts the total gamma in billions per 1% of SPX move. There is a large amount of put gamma.

Majority of calls are from covered calls (when an investor buys the underlying stock and sells the a call option). The market maker is long calls and short on the underlying stock. As price of the underlying stock increases, delta needs to be hedged and the market maker will sell.

Refer to this link to learn more about GEX (and its interpretation): https://help.quantdata.us/en/articles/7852449-what-is-gamma-exposure-gex

Call and Put GEX Chart (for options)

cpGEX <- plot_ly(data = df, x = ~StrikePrice, y = ~CallGEX, type = 'bar', name = 'Call Gamma')
cpGEX <- cpGEX %>% add_bars(
  x = ~StrikePrice,
  y = ~PutGEX,
  marker = list(color = 'red'),
  name = 'Put Gamma'
)
cpGEX <- cpGEX %>% add_lines(x=spotPrice,
                               line=list(color= 'blue', dash = 'dot'),
                               name= paste('Spot Price: ', round(spotPrice,0))
)
cpGEX <- cpGEX %>%layout(
  title = paste("Total Gamma: $", TotalGam,  " Bn per 1% SPX Move ",  todayDate),
  xaxis=list(title='Strike Price'),
  yaxis=list(title='Spot Gamma Exposure ($ billions/1% move)')
)
cpGEX

Call Put Gex Charts for Stocks

AAPL

#obtain the last price
aaplOptChain <-fread("./aapl_quotedata.csv")
spotLine1 <- fread("./aapl_quotedata.csv", skip=3, nrows = 1)
spotPrice1 <- as.numeric((spotLine1[1,3]))
spotPrice1

## [1] 142.9

#spotPrice range
fromS1 <- 0.8 * spotPrice1
toS1 <- 2.3 * spotPrice1
todaysDateData1 <-strsplit(as.character(spotLine1[,1]), " ")
todayYear1 <- todaysDateData1[[1]][4]
todayMonth1 <- todaysDateData1[[1]][2]
todayDay1 <- todaysDateData1[[1]][3]
todayDate1 <-as.Date(ymd(paste0(todayYear1, '-', todayMonth1, '-', todayDay1)))
todayDate1

## [1] "2023-12-29"

colnames(aaplOptChain) <-c('ExpirationDate','Calls','CallLastSale','CallNet','CallBid','CallAsk','CallVol',
'CallIV','CallDelta','CallGamma','CallOpenInt','StrikePrice','Puts','PutLastSale',
'PutNet','PutBid','PutAsk','PutVol','PutIV','PutDelta','PutGamma','PutOpenInt')
aaplOptChain$CallGEX <- aaplOptChain$CallGamma * 100 * aaplOptChain$CallOpenInt * spotPrice1^2 *0.01
aaplOptChain$PutGEX <- aaplOptChain$PutGamma * 100 * aaplOptChain$PutOpenInt * spotPrice1^2 *0.01 * -1
aaplOptChain$TotalGamma <- (aaplOptChain$CallGEX + aaplOptChain$PutGEX) / 971
df1 <- aaplOptChain %>% 
  filter(StrikePrice > fromS1, StrikePrice < toS1) %>%
  group_by(StrikePrice) %>% 
  summarise(CallGEX = sum(CallGEX), PutGEX = sum(PutGEX), TotalGamma = sum(TotalGamma))
strike1 <- aaplOptChain$StrikePrice
TotalGam1 <- round(sum(df1$TotalGamma),3)
aaplSpotExpChart <- plot_ly(df1,x= ~df1$StrikePrice, y = ~df1$TotalGamma, type = "bar", name = "Total Gamma")
aaplSpotExpChart <- aaplSpotExpChart %>% add_lines(x=spotPrice1, line=list(color= "blue", dash = 'dot'), name= paste('Spot Price: ', round(spotPrice1,0)))
aaplSpotExpChart <- aaplSpotExpChart %>%layout(
  title = paste("Total Gamma: $", TotalGam1,  " AAPL",  todayDate1),
  xaxis=list(title='Strike Price'),
  yaxis=list(title='Spot Gamma Exposure ')
)
aaplSpotExpChart

aaplCPGEX <- plot_ly(data = df1, x = ~StrikePrice, y = ~CallGEX, type = 'bar', name = 'Call Gamma')
aaplCPGEX <- aaplCPGEX %>% add_bars(x = ~StrikePrice,
y = ~PutGEX, marker = list(color = 'red'), name = 'Put Gamma')
aaplCPGEX <- aaplCPGEX %>% add_lines(x=spotPrice1,
 line=list(color= 'blue', dash = 'dot'), name= paste('Spot Price: ', round(spotPrice1,0)))
aaplCPGEX <- aaplCPGEX %>%layout(title = paste("Total Gamma: $", TotalGam1,  "AAPL Move ",  todayDate1), xaxis=list(title='Strike Price'),yaxis=list(title='Spot Gamma Exposure'))
aaplCPGEX

Analysis:

Interest in Small and Midcap stocks

SOFI

#obtain the last price
sofiOptChain <-fread("./sofi_quotedata.csv")
spotLine2 <- fread("./sofi_quotedata.csv", skip=3, nrows = 1)
spotPrice2 <- as.numeric((spotLine2[1,3]))
spotPrice2

## [1] 7.72

#spotPrice range
fromS2 <- 0.7 * spotPrice2
toS2 <-2.5 * spotPrice2
todaysDateData2 <-strsplit(as.character(spotLine2[,1]), " ")
todayYear2 <- todaysDateData2[[1]][4]
todayMonth2 <- todaysDateData2[[1]][2]
todayDay2 <- todaysDateData2[[1]][3]
todayDate2 <-as.Date(ymd(paste0(todayYear2, '-', todayMonth2, '-', todayDay2)))
colnames(sofiOptChain) <-c('ExpirationDate','Calls','CallLastSale','CallNet','CallBid','CallAsk','CallVol',
'CallIV','CallDelta','CallGamma','CallOpenInt','StrikePrice','Puts','PutLastSale',
'PutNet','PutBid','PutAsk','PutVol','PutIV','PutDelta','PutGamma','PutOpenInt')
sofiOptChain$CallGEX <- sofiOptChain$CallGamma * 100 * sofiOptChain$CallOpenInt * spotPrice2^2 *0.01
sofiOptChain$PutGEX <- sofiOptChain$PutGamma * 100 * sofiOptChain$PutOpenInt * spotPrice2^2 *0.01 * -1
sofiOptChain$TotalGamma <- (sofiOptChain$CallGEX + sofiOptChain$PutGEX) / 327
df2 <- sofiOptChain %>% 
  filter(StrikePrice > fromS2, StrikePrice < toS2) %>%
  group_by(StrikePrice) %>% 
  summarise(CallGEX = sum(CallGEX), PutGEX = sum(PutGEX), TotalGamma = sum(TotalGamma))
strike2 <- sofiOptChain$StrikePrice
TotalGam2 <- round(sum(df2$TotalGamma),3)
sofiSpotExpChart <- plot_ly(df2,x= ~df2$StrikePrice, y = ~df2$TotalGamma, type = "bar", name = "Total Gamma")
sofiSpotExpChart <- sofiSpotExpChart %>% add_lines(x=spotPrice2, line=list(color= "blue", dash = 'dot'), name= paste('Spot Price: ', round(spotPrice2,0)))
sofiSpotExpChart <- sofiSpotExpChart %>%layout(
  title = paste("Total Gamma: $", TotalGam2,  " SOFI ",  todayDate2),
  xaxis=list(title='Strike Price'),
  yaxis=list(title='Spot Gamma Exposure ')
)
sofiSpotExpChart

sofiCPGEX <- plot_ly(data = df2, x = ~df2$StrikePrice, y = ~df2$CallGEX, type = 'bar', name = 'Call Gamma')
sofiCPGEX <- sofiCPGEX %>% add_bars(x = ~df2$StrikePrice,
y = ~df2$PutGEX, marker = list(color = 'red'), name = 'Put Gamma')
sofiCPGEX <- sofiCPGEX %>% add_lines(x=spotPrice2,
 line=list(color= 'blue', dash = 'dot'), name= paste('Spot Price: ', round(spotPrice2,0)))
sofiCPGEX <- sofiCPGEX %>%layout(title = paste("Total Gamma: $", TotalGam2,  "SOFI Move ",  todayDate2), xaxis=list(title='Strike Price'),yaxis=list(title='Spot Gamma Exposure'))
sofiCPGEX

Analysis:

LCID

#obtain the last price
lcidOptChain <-fread("./lcid_quotedata.csv")
spotLine3 <- fread("./lcid_quotedata.csv", skip=3, nrows = 1)
spotPrice3 <- as.numeric((spotLine3[1,3]))
spotPrice3

## [1] 3.8

#spotPrice range
fromS3 <- 0.4 * spotPrice3
toS3 <- 2.8 * spotPrice3
todaysDateData3 <-strsplit(as.character(spotLine3[,1]), " ")
todayYear3 <- todaysDateData3[[1]][4]
todayMonth3 <- todaysDateData3[[1]][2]
todayDay3 <- todaysDateData3[[1]][3]
todayDate3 <-as.Date(ymd(paste0(todayYear3, '-', todayMonth3, '-', todayDay3)))
colnames(lcidOptChain) <-c('ExpirationDate','Calls','CallLastSale','CallNet','CallBid','CallAsk','CallVol',
'CallIV','CallDelta','CallGamma','CallOpenInt','StrikePrice','Puts','PutLastSale',
'PutNet','PutBid','PutAsk','PutVol','PutIV','PutDelta','PutGamma','PutOpenInt')
lcidOptChain$CallGEX <- lcidOptChain$CallGamma * 100 * lcidOptChain$CallOpenInt * spotPrice3^2 *0.01
lcidOptChain$PutGEX <- lcidOptChain$PutGamma * 100 * lcidOptChain$PutOpenInt * spotPrice3^2 *0.01 * -1
lcidOptChain$TotalGamma <- (lcidOptChain$CallGEX + lcidOptChain$PutGEX) / 345
df3 <- lcidOptChain %>% 
  filter(StrikePrice > fromS3, StrikePrice < toS3) %>%
  group_by(StrikePrice) %>% 
  summarise(CallGEX = sum(CallGEX), PutGEX = sum(PutGEX), TotalGamma = sum(TotalGamma))
strike3 <- lcidOptChain$StrikePrice
TotalGam3 <- round(sum(df3$TotalGamma),3)
lcidSpotExpChart <- plot_ly(df3,x= ~df3$StrikePrice, y = ~df3$TotalGamma, type = "bar", name = "Total Gamma")
lcidSpotExpChart <- lcidSpotExpChart %>% add_lines(x=spotPrice3, line=list(color= "blue", dash = 'dot'), name= paste('Spot Price: ', round(spotPrice3,0)))
lcidSpotExpChart <- lcidSpotExpChart %>%layout(
  title = paste("Total Gamma: $", TotalGam3,  " LCID ",  todayDate3),
  xaxis=list(title='Strike Price'),
  yaxis=list(title='Spot Gamma Exposure ')
)
lcidSpotExpChart

lcidCPGEX <- plot_ly(data = df3, x = ~df3$StrikePrice, y = ~df3$CallGEX, type = 'bar', name = 'Call Gamma')
lcidCPGEX <- lcidCPGEX %>% add_bars(x = ~df3$StrikePrice,
y = ~df3$PutGEX, marker = list(color = 'red'), name = 'Put Gamma')
lcidCPGEX <- lcidCPGEX %>% add_lines(x=spotPrice3,
 line=list(color= 'blue', dash = 'dot'), name= paste('Spot Price: ', round(spotPrice3,0)))
lcidCPGEX <- lcidCPGEX %>%layout(title = paste("Total Gamma: $", TotalGam2,  "LCID ",  todayDate3), xaxis=list(title='Strike Price'),yaxis=list(title='Spot Gamma Exposure'))
lcidCPGEX