lazytrade

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!

Installation

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

• Data manipulation and analysis of performed trades results
• Reinforcement Learning for Automated Trading Risk Management
• Data manipulation and preparation for Machine Learning (transposing, aggregation, lagging, etc)
• Using Deep Learning for prediction of Market Types (Classification)
• Using Deep Learning for prediction of future price change (Regression)
• Strategy Tests simulations
• Utility functions to generate passwords, initialization files, encryption of passwords, etc
• Explored idea of building a model using random structures combined with an automated functional (strategy) test to improve model performance
• Overall, all functions have working examples with relevant documented sample data included in the package

Example - prepare data for machine learning

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 results

Multiple 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 ac5cE049

Example - 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

This readme file

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 changes

taken 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:

#’ #’ #’

Generating Documentation

Title of the package

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”

Re-generating documentation

Run this code to re-generate documentation devtools::document()

Fixing License

Run this code to fix license: usethis::use_mit_license(name = "Vladimir Zhbanko")

Adding data to the package for internal tests

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)

Adding files to the package

Place data like small files to the folder: inst/extdata

Adding examples to test package function

Tests setup first time

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 in Roxygen code

@examples …

code to execute during package checks

@examples

/donttest{

code to NOT execute during package checks

}

Testing a package

Create a test script

Run this command to create a new script with the test skeleton:

usethis::use_test("profit_factor.R")

Enrich the test script

Details:

1. add libraries used for test
2. add function context("profit_factor")
3. add function test_that(“test description”, {test process})
4. load data using function 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