- Load libraries
# lirary load
Packages <- c("tidyverse", "DBI", "data.table", "readr", "IBrokers", "RMySQL", "ggplot2", "ggExtra")
lapply(Packages, library, character.only = TRUE)## Warning: package 'tidyverse' was built under R version 3.5.2## -- Attaching packages --------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.8
## v tidyr 0.8.2 v stringr 1.3.1
## v readr 1.3.1 v forcats 0.3.0## Warning: package 'ggplot2' was built under R version 3.5.2## Warning: package 'tidyr' was built under R version 3.5.2## Warning: package 'readr' was built under R version 3.5.2## Warning: package 'dplyr' was built under R version 3.5.2## Warning: package 'forcats' was built under R version 3.5.2## -- Conflicts ------------------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## Warning: package 'DBI' was built under R version 3.5.2## Warning: package 'data.table' was built under R version 3.5.2##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
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## transpose## Loading required package: xts## Loading required package: zoo##
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## first, last## IBrokers version 0.9-10. Implementing API Version 9.64##
## IBrokers comes with NO WARRANTY. Not intended for production use!## See ?IBrokers for details.## Warning: package 'RMySQL' was built under R version 3.5.2## [[1]]
## [1] "forcats" "stringr" "dplyr" "purrr"
## [5] "readr" "tidyr" "tibble" "ggplot2"
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## [13] "tibble" "ggplot2" "tidyverse" "RevoUtils"
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## [13] "tibble" "ggplot2" "tidyverse" "RevoUtils"
## [17] "stats" "graphics" "grDevices" "utils"
## [21] "datasets" "RevoUtilsMath" "methods" "base"
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## [1] "ggExtra" "RMySQL" "IBrokers" "xts"
## [5] "zoo" "data.table" "DBI" "forcats"
## [9] "stringr" "dplyr" "purrr" "readr"
## [13] "tidyr" "tibble" "ggplot2" "tidyverse"
## [17] "RevoUtils" "stats" "graphics" "grDevices"
## [21] "utils" "datasets" "RevoUtilsMath" "methods"
## [25] "base"1.3 Download from library
con <- dbConnect(RMySQL::MySQL(),
dbname= "quantmei_library",
host = "91.208.99.2",
port = 1210,
user = "quantmei_library",
password = "Antonio2357!")
OHLC_DAILY_P_IV_SPX <- dbReadTable(con, "OHLC_DAILY_P_IV_SPX")- Created calculated values Name Description
R_co range at close of close to open C - O R-hl range of high to low at close H - L E_h expected H O * (1+(v/SQRT(252)) E_l expected L O * (1-(v/SQRT(252)) H_E_h diff from H to E.H [O * (1+v)] - H L_E_l diff from E.L to L [O * (1-v)] - L R_2 theoretical range expected Library Call back (given constant) S_UP upper bear spread strike E.H +/- CONSTANT S_DW lower bull spread strike E.L +/- CONSTANT C_ER.H calculated expected error for high C_ER.L calculated expected error for low IV_d daily IV as a function of the yearly [[ IV Y]]/SQRT(252) H_o high from open deviation [O * (1+v)] - (H - O) L_o low from open deviation [O * (1+v)] - (H - O) E_h_o expected high dev from open E_L_o expected low dev from open
OHLC_DAILY_P_IV_SPX$R_co <- OHLC_DAILY_P_IV_SPX$SPX.Close.x - OHLC_DAILY_P_IV_SPX$SPX.Open.x
OHLC_DAILY_P_IV_SPX$R_hl <- OHLC_DAILY_P_IV_SPX$SPX.High.x - OHLC_DAILY_P_IV_SPX$SPX.Low.x
OHLC_DAILY_P_IV_SPX$IV_d <- OHLC_DAILY_P_IV_SPX$SPX.Open.y / sqrt(252)
OHLC_DAILY_P_IV_SPX$E_h <- OHLC_DAILY_P_IV_SPX$SPX.Open.x * ( 1 + OHLC_DAILY_P_IV_SPX$IV_d)
OHLC_DAILY_P_IV_SPX$E_l <- OHLC_DAILY_P_IV_SPX$SPX.Open.x * ( 1 - OHLC_DAILY_P_IV_SPX$IV_d)
OHLC_DAILY_P_IV_SPX$H_E_h <- OHLC_DAILY_P_IV_SPX$E_h - OHLC_DAILY_P_IV_SPX$SPX.High.x
OHLC_DAILY_P_IV_SPX$L_E_l <- OHLC_DAILY_P_IV_SPX$E_h - OHLC_DAILY_P_IV_SPX$SPX.Low.x
OHLC_DAILY_P_IV_SPX$H_o <- OHLC_DAILY_P_IV_SPX$SPX.High.x - OHLC_DAILY_P_IV_SPX$SPX.Open.x
OHLC_DAILY_P_IV_SPX$L_o <- OHLC_DAILY_P_IV_SPX$SPX.Open.x - OHLC_DAILY_P_IV_SPX$SPX.Low.x
OHLC_DAILY_P_IV_SPX$E_h_o <- OHLC_DAILY_P_IV_SPX$E_h - OHLC_DAILY_P_IV_SPX$SPX.Open.x
OHLC_DAILY_P_IV_SPX$E_l_o <- OHLC_DAILY_P_IV_SPX$SPX.Open.x - OHLC_DAILY_P_IV_SPX$SPX.Low.x
OHLC_DAILY_P_IV_SPX$E_R_hl <- OHLC_DAILY_P_IV_SPX$E_h - OHLC_DAILY_P_IV_SPX$E_l
OHLC_DAILY_P_IV_SPX$Diff_R <- OHLC_DAILY_P_IV_SPX$E_R_hl - OHLC_DAILY_P_IV_SPX$R_hlTime series of [ Close - Open ] over time
# classic plot :
ggplot(OHLC_DAILY_P_IV_SPX, aes(x=Date, y=R_co)) +
geom_point(col="blue", size = 1) +
geom_line(data=OHLC_DAILY_P_IV_SPX, aes(x=Date, y=SPX.Open.y), col="yellow", size = 1)## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
Frequency distribution of R_o
hist(OHLC_DAILY_P_IV_SPX$R_co)
hist(OHLC_DAILY_P_IV_SPX$R_co,breaks = 50 , freq=TRUE)
Frequency distribution of Daily IV
hist(OHLC_DAILY_P_IV_SPX$SPX.Open.y,breaks = 50 , freq=TRUE)
Cumulative histogram of IV
hist( OHLC_DAILY_P_IV_SPX$SPX.Open.y ) -> h # do a histogram of y and assign its info to h
h$counts <- cumsum(h$counts) # replace the cell freq.s by cumulative freq.s
plot( h, labels = TRUE ) # plot a cumulative histogram of y
Cumulative histogram of IV v2
eom_point(size = 0.01)Time series of range to expected range
ggplot(OHLC_DAILY_P_IV_SPX, aes(x=Date, OHLC_DAILY_P_IV_SPX$R_hl), col = "blue") +
geom_point(size = 0.1) +
geom_point(aes(x=Date, OHLC_DAILY_P_IV_SPX$E_R_hl), col = "red", size = 0.1)
Time series of realised to expcted range
ggplot(OHLC_DAILY_P_IV_SPX, aes(x=Date, OHLC_DAILY_P_IV_SPX$Diff_R), col = "blue") +
geom_point(size = 0.1) +
geom_point(aes(x=Date, OHLC_DAILY_P_IV_SPX$IV_d), col = "red", size = 0.1)
overlay plot #1
# p <- ggplot(OHLC_DAILY_P_IV_SPX, aes(x=Date, OHLC_DAILY_P_IV_SPX$R_hl)) +
# geom_point() +
# geom_line(OHLC_DAILY_P_IV_SPX, aes(x=Date, OHLC_DAILY_P_IV_SPX$IV_d))
# Create a simple secondary axis
# p + scale_y_continuous(sec.axis = sec_axis(~.+10))
p <- ggplot(OHLC_DAILY_P_IV_SPX, aes(x=Date, y=R_co)) +
geom_point(col="blue", size = 0.1) +
geom_point(data=OHLC_DAILY_P_IV_SPX, aes(x=Date, y=SPX.Open.y), col="red", size = 0.1)
p + scale_y_continuous(sec.axis = sec_axis(~./1000000))