The goal of lazytrade is to keep all functions and scripts of the lazytrade educational project on UDEMY. Functions are providing an opportunity to learn Computer and Data Science using example of Algorithmic Trading. Please kindly not that this project was created for Educational Purposes only!
You can install the released version of lazytrade from CRAN with:
install.packages("lazytrade")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("vzhomeexperiments/lazytrade") Several ideas explored in this package
This is a basic example which shows you how to solve a common problem:
library(lazytrade) library(magrittr, warn.conflicts = FALSE) ## basic example code # Convert a time series vector to matrix with 64 columns macd_m <- seq(1:1000) %>% as.data.frame() %>% to_m(20) head(macd_m, 2) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] #> [1,] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 #> [2,] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 #> [,15] [,16] [,17] [,18] [,19] [,20] #> [1,] 15 16 17 18 19 20 #> [2,] 35 36 37 38 39 40
Why is it useful? It is possible to convert time-series data into matrix data to do make modeling
Example - aggregate multiple log files and visualize resultsMultiple log files could be joined into one data object
library(lazytrade)
library(readr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(magrittr)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
# files are located in the sample folders
DFOLDER <- system.file("extdata/RES", package = "lazytrade")
DFR <- opt_aggregate_results(path_data = DFOLDER)
This data object can be visualized
library(ggplot2) opt_create_graphs(x = DFR, outp_path = dir,graph_type = 'bars')
Or just visualize results with time-series plot
opt_create_graphs(x = DFR, outp_path = dir,graph_type = 'ts')Example - leverage Reinforcement Learning for Risk Management
Example below would generate RL policy based on the trade results achieved so far
library(dplyr)
library(ReinforcementLearning)
library(magrittr)
data(data_trades)
states <- c("tradewin", "tradeloss")
actions <- c("ON", "OFF")
control <- list(alpha = 0.7, gamma = 0.3, epsilon = 0.1)
rl_generate_policy(data_trades, states, actions, control)
#> TradeState Policy
#> tradeloss tradeloss ON
#> tradewin tradewin OFF Example - generating passwords for trading platforms login
Multiple trading accounts require passwords, package contains function that may easily generate random passwords:
library(lazytrade) library(stringr) library(magrittr) library(openssl) library(readr) #generate 8digit password for trading platform util_generate_password(salt = 'random text') #> . #> 1 ac5cE049Example - generate initialization files for MT4 platform
Facilitate generation of initialisation files:
library(lazytrade)
dir <- normalizePath(tempdir(),winslash = "/")
# test file to launch MT4 terminal with parameters
write_ini_file(mt4_Profile = "Default",
mt4_Login = "12345678",
mt4_Password = "password",
mt4_Server = "BrokerServerName",
dss_inifilepath = dir,
dss_inifilename = "prod_T1.ini",
dss_mode = "prod") Notes to remind myself how to create R package
What is special about using README.Rmd instead of just README.md? You can include R chunks like so:
summary(cars) #> speed dist #> Min. : 4.0 Min. : 2.00 #> 1st Qu.:12.0 1st Qu.: 26.00 #> Median :15.0 Median : 36.00 #> Mean :15.4 Mean : 42.98 #> 3rd Qu.:19.0 3rd Qu.: 56.00 #> Max. :25.0 Max. :120.00
You’ll still need to render README.Rmd regularly, to keep README.md up-to-date.
taken from https://r-pkgs.org/
Communicate about lifecycle changestaken from https://lifecycle.r-lib.org/articles/communicate.html
Run Once:
usethis::use_lifecycle()
To insert badge:
Add badges in documentation topics by inserting one of:
#’ #’ #’
Create right title case for the title of the package By running this command… tools::toTitleCase("Learn computer and data science using algorithmic trading") the Title will become: “Learn Computer and Data Science using Algorithmic Trading”
Run this code to re-generate documentation devtools::document()
Run this code to fix license: usethis::use_mit_license(name = "Vladimir Zhbanko")
Run this code to add data to the folder data/ x <- sample(1000) usethis::use_data(x)
To update this data: x <- sample(2000) usethis::use_data(x, overwrite = T)
To convert character into time: mutate(across('X1', ~ as.POSIXct(.x, format = "%Y.%m.%d %H:%M:%S")))
Note: use option ’LazyLoad` to make data available only when user wants it always include LazyData: true in your DESCRIPTION. Note: to document dataset see https://stackoverflow.com/questions/2310409/how-can-i-document-data-sets-with-roxygen
Document dataset using the R script R/datasets.R
Use data in the function with data(x)
Place data like small files to the folder: inst/extdata
Run this command to setup tests ‘usethis::use_testthat()’
This will create a folder with the name tests
Inside this folder there will be another folder testthat.
@examples …
code to execute during package checks
@examples
/donttest{
code to NOT execute during package checks
}
Run this command to create a new script with the test skeleton:
usethis::use_test("profit_factor.R")
Details:
context("profit_factor")data(named_data_object)Example:
library(testthat)
#>
#> Attaching package: 'testthat'
#> The following object is masked from 'package:dplyr':
#>
#> matches
#> The following objects are masked from 'package:readr':
#>
#> edition_get, local_edition
#> The following objects are masked from 'package:magrittr':
#>
#> equals, is_less_than, not
library(dplyr)
library(magrittr)
context("profit_factor")
test_that("test value of the calculation", {
data(profit_factor_data)
DF_Stats <- profit_factor_data %>%
group_by(X1) %>%
summarise(PnL = sum(X5),
NumTrades = n(),
PrFact = util_profit_factor(X5)) %>%
select(PrFact) %>%
head(1) %>%
pull(PrFact) %>%
round(3)
expect_equal(DF_Stats, 0.68)
})
#> Test passed 🎊 Test of the coverage for the script
Test coverage shows you what you’ve tested devtools::test_coverage_file()
devtools::test_coverage_file()
This will add automatic test coverage badge to the readme file on github usethis::use_coverage()
Step 1. devtools::document() Step 2. devtools::run_examples() Step 3. Menu ‘Build’ Clean and Rebuild Step 4. ‘Check’ devtools::check()
This is now a default option
Whenever examples construct is used author of the package must insure that those examples are running. Such examples are those that would require longer test execution. To perform this test package needs to be checked with the following command:
devtools::check(run_dont_test = TRUE)
whenever a quick check is required:
devtools::check(run_dont_test = FALSE) ???
In case functions are writing files there are few considerations to take into account:
tempdir() functiondevtools::check() there should nothing remain in the ‘tmp/’ directoryFile names defined by function tempdir() would look like this:
# > tempdir() # [1] "/tmp/RtmpkaFStZ"
File names defined by function tempfile() would look like this:
# > tempfile() # [1] "/tmp/RtmpkaFStZ/file7a33be992b4"
This is example of how function write_csv example works:
tmp <- tempfile() write_csv(mtcars, tmp)
results of this code are correctly stored to the temporary file
however this example from readr package function write_csv is showing that file will be written to the ‘/tmp/’ directory
dir <- tempdir() write_tsv(mtcars, file.path(dir, "mtcars.tsv.gz"))Deleting files after running examples:
We use function unlink() to do this:
unlink("/tmp/*.csv", recursive = TRUE, force = TRUE)
and we check that there is nothing more remained:
Delete deprecate functionsTo remove function from the package we can use:
see https://stackoverflow.com/questions/9439256/how-can-i-handle-r-cmd-check-no-visible-binding-for-global-variable-notes-when see https://github.com/HughParsonage/grattan/blob/master/R/zzz.R
Unfortunate note on specific flavorsAfter first submission there are some notes on specific R flavors
This question was addressed here but yet it’s not answered: https://stackoverflow.com/questions/48487541/r-cmd-check-note-namespace-in-imports-field-not-imported
To search for specific function in the scripts one can do the following:
list_of_functions <- c(
"drop_na",
"fill",
"extract",
"gather",
"nest",
"separate"
)
for (FN in list_of_functions) {
if(!exists("res")){
res <- BurStMisc::scriptSearch(FN)
} else {
res2 <- BurStMisc::scriptSearch(FN)
res <- mapply(c, res, res2, SIMPLIFY=FALSE)}
}
When functions are writing to the file
It’s important to avoid that function write to the directory other then tempdir() Construct file name must be done using file.name() function as follow:
# use plane temp directory
dir_name <- normalizePath(tempdir(),winslash = "/")
file_name <- paste0('my_file', 1, '.csv')
# this needs to be used in the function
full_path <- file.path(dir_name, file_name)
# when using sub-directory
sub_dir <- file.path(dir_name, "_SUB")
if(!dir.exists(sub_dir)){dir.create(sub_dir)} Versioning of the package
https://r-pkgs.org/description.html#version
..
Clone package from GitHub and test check it in Docker Container
devtools::build()
usethis::use_readme_rmd()
usethis::use_github_action()
To be elaborated
Setup the new version of the package:
usethis::use_release_issue()
Follow checklist before upload to CRAN:
devtools::release_checks()
then:
devtools::release()
spelling devtools::spell_check()
See ?rhubv2
devtools::check_win_release()
devtools::check_win_devel()
devtools::check_win_oldrelease()
Explain the changes
uploading the package archive to CRANhttps://cran.r-project.org/submit.html
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