added test data for the function

_RL/TradeTriggerRL.R

@@ -11,7 +11,7 @@
 # packages used *** make sure to install these packages
 library(tidyverse) #install.packages("tidyverse")
 library(lubridate) #install.packages("lubridate") 
-library(ReinforcementLearning) #devtools::install_github("nproellochs/ReinforcementLearning")
+library(ReinforcementLearning) #install.packages("ReinforcementLearning")
 library(magrittr)
 
 # ----------- Main Steps -----------------
@@ -76,7 +76,8 @@ for (i in 1:length(vector_systems)) {
  # tryCatch() function will not abort the entire for loop in case of the error in one iteration
  tryCatch({
  # execute this code below for debugging:
- # i <- 5
+ # i <- 7 #policy off
+ # i <- 2 #policy on
 
  # extract current magic number id
  trading_system <- vector_systems[i]
@@ -102,8 +103,8 @@ for (i in 1:length(vector_systems)) {
  # NOTE: more research is required to find best parameters TDL TDL TDL
  #control <- list(alpha = 0.5, gamma = 0.5, epsilon = 0.5)
  #control <- list(alpha = 0.9, gamma = 0.9, epsilon = 0.9)
- control <- list(alpha = 0.1, gamma = 0.2, epsilon = 0.5)
- #control <- list(alpha = 0.3, gamma = 0.6, epsilon = 0.1) 
+ #control <- list(alpha = 0.7, gamma = 0.5, epsilon = 0.9)
+ control <- list(alpha = 0.3, gamma = 0.6, epsilon = 0.1) 
 
  # perform reinforcement learning and return policy
  policy_tr_systDF <- generate_RL_policy(trading_systemDF, states = states,actions = actions,

_RL/generate_RL_policy.R

@@ -17,6 +17,7 @@
 generate_RL_policy <- function(x, states, actions, control){
  require(tidyverse)
  require(ReinforcementLearning)
+ require(magrittr)
  # uncomment to debug code inside the function
  # x <- read_rds("_TEST_DATA/data_trades.rds")
  # x <- trading_systemDF
@@ -63,15 +64,20 @@ generate_RL_policy <- function(x, states, actions, control){
  s_new = "NextState", control = control, iter = 1, model = model)
  #model$Q
  #print(i)
+
  }
+
+ #plot(model)
+
  # extract custom policy from the obtained dataset
  df_Q <- model$Q %>% as.data.frame() %>% 
  # create column with market periods
  mutate(TradeState = row.names(.)) %>% 
  # interpret policy as defined logic, value at ON must be >= 0!
  mutate(Policy = ifelse(ON <= 0, "OFF", ifelse(ON > OFF, "ON", ifelse(OFF > ON, "OFF", NA)))) %>% 
- select(TradeState, Policy)
-
+ select(TradeState, Policy) 
+ # record this object for the function debugging
+ # write_rds(df_Q, "_TEST_DATA/TradeStatePolicy.rds")
  #plot(model)
  return(df_Q)
 

_RL/record_policy.R

@@ -10,13 +10,16 @@
 #' @param last_result - character vector of the last result of the trade
 #' 
 #' @return nothing is returned
-#' @export function creates csv file
+#' @export - function creates csv file
 #' @example record_policy(x = policy_tr_systDF, trading_system = trading_system, path_sandbox = path_T4)
 #' 
 record_policy <- function(x, last_result, trading_system, path_sandbox){
+ require(tidyverse)
+ require(magrittr)
  # debugging
- # trading_system <- 8118105
+ # trading_system <- 8118101
  # last_result <- "tradeloss"
+ # x <- read_rds("_TEST_DATA/TradeStatePolicy.rds")
  # x <- policy_tr_systDF
  # path_sandbox <- "C:/Program Files (x86)/FxPro - Terminal3/MQL4/Files/"
 # derive which terminal should be enabled (using path to sandbox) and using variable 'addition'