Merge pull request #23 from vzhomeexperiments/dev_0.4.1

Dev 0.4.1

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: lazytrade
 Type: Package
 Title: Learn Computer and Data Science using Algorithmic Trading
-Version: 0.4.0
+Version: 0.4.1
 Author: Vladimir Zhbanko
 Maintainer: Vladimir Zhbanko <[email protected]>
 Description: Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading.
@@ -26,8 +26,7 @@ Imports:
     grDevices,
     h2o,
     ReinforcementLearning,
-    openssl,
-    tidyr
+    openssl
 Suggests: 
     testthat (>= 2.1.0),
     covr,

NAMESPACE

@@ -10,27 +10,27 @@ export(create_transposed_data)
 export(decrypt_mykeys)
 export(encrypt_api_key)
 export(evaluate_macroeconomic_event)
-export(evaluate_market_type)
-export(generate_RL_policy)
-export(generate_RL_policy_mt)
 export(get_profit_factorDF)
 export(import_data)
 export(import_data_mt)
 export(load_asset_data)
-export(log_RL_progress)
-export(log_RL_progress_mt)
+export(mt_evaluate)
+export(mt_import_data)
 export(mt_make_model)
 export(opt_aggregate_results)
 export(opt_create_graphs)
-export(profit_factor)
-export(record_policy)
-export(record_policy_mt)
+export(rl_generate_policy)
+export(rl_generate_policy_mt)
+export(rl_log_progress)
+export(rl_log_progress_mt)
+export(rl_record_policy)
+export(rl_record_policy_mt)
+export(rl_write_control_parameters)
+export(rl_write_control_parameters_mt)
 export(self_learn_ai_R)
 export(test_model)
 export(to_m)
 export(util_generate_password)
-export(writeCommandViaCSV)
+export(util_profit_factor)
 export(write_command_via_csv)
-export(write_control_parameters)
-export(write_control_parameters_mt)
 export(write_ini_file)

NEWS.md

@@ -3,10 +3,22 @@
 ## Planned Changes
 
 * Add folder generation function
-* Remove warnings:
 
-1: `as_data_frame()` is deprecated as of tibble 2.0.0. Please use `as_tibble()` instead.
-2: `data_frame()` is deprecated as of tibble 1.1.0. Please use `tibble()` instead.
+# lazytrade 0.4.1
+
+# Version 0.4.1
+
+## Changes
+
+### Implemented
+
+* added option to balance classes in mt_make_model function
+* add and delete depricated functions
+* change names of functions to reflect the purpose: mt - market type, rl- reinforcement learning, util - utility
+* change example methods to unify them across functions
+* align names of the variables across several functions
+* remove warnings caused by new dplyr functionalities .groups
+* remove tidyr from imports to solve Note
 
 # lazytrade 0.4.0
 

R/aml_make_model.R

@@ -53,8 +53,8 @@
 #'
 #' # dataset will be written to the temp directory
 #'
-#' # start h2o engine (using all CPU's by default)
-#' h2o.init()
+#' # start h2o engine
+#' h2o.init(nthreads = 2)
 #'
 #'
 #' # performing Deep Learning Regression using the custom function

R/aml_score_data.R

@@ -48,7 +48,7 @@
 #'                  path_data = path_data)
 #' # start h2o engine (using all CPU's by default)
 #'
-#' h2o.init()
+#' h2o.init(nthreads = 2)
 #'
 #'
 #' # performing Deep Learning Regression using the custom function

R/aml_test_model.R

@@ -54,8 +54,8 @@
 #'                  path_data = path_data)
 #'
 #'
-#' # start h2o engine (using all CPU's by default)
-#' h2o.init()
+#' # start h2o engine
+#' h2o.init(nthreads = 2)
 #'
 #'
 #' # performing Deep Learning Regression using the custom function
@@ -116,14 +116,14 @@ aml_test_model <- function(symbol, num_bars, timeframe, path_model, path_data,
   #dataset with date column X1
   y <- readr::read_rds(full_path) %>%
     # remove empty rows
-    na.omit() %>% filter_all(any_vars(. != 0))
+    na.omit() %>% dplyr::filter_all(any_vars(. != 0))
 
 
   #dataset without X1 column (for predictions)
   x <- y  %>%
-    select(-X1, -X2, -X3, -LABEL) %>%
+    dplyr::select(-X1, -X2, -X3, -LABEL) %>%
     # only keep last month for simulation
-    head(num_bars)
+    utils::head(num_bars)
 
   # generate a file name for model
   m_name <- paste0("DL_Regression", "-", symbol,"-", timeframe)
@@ -161,7 +161,7 @@ aml_test_model <- function(symbol, num_bars, timeframe, path_model, path_data,
 
   dat31 <- y %>%
     # using last 600 observations
-    head(num_bars) %>%
+    utils::head(num_bars) %>%
     ## select columns:
     # X1 time index
     # X2 price at the time index
@@ -227,10 +227,10 @@ aml_test_model <- function(symbol, num_bars, timeframe, path_model, path_data,
 
 
   df_tr <- df_res %>%
-    group_by(TR_Level, NB_hold, Symbol) %>%
-    summarise(MaxPerf = max(PnL_NB)) %>%
-    arrange(desc(MaxPerf)) %>%
-    head(1)
+    dplyr::group_by(TR_Level, NB_hold, Symbol) %>%
+    dplyr::summarise(MaxPerf = max(PnL_NB)) %>%
+    dplyr::arrange(desc(MaxPerf)) %>%
+    utils::head(1)
 
 
 

R/check_if_optimize.R

@@ -9,58 +9,58 @@
 #' @details Whenever there will be not enough trades then empty file will be written to the destination
 #'
 #' @param x - dataframe containing trading results
-#' @param path_trading_robot - path of trading robot repository. must contain folder TEST with file Setup.csv.
-#' File Setup.csv contains a table with magic numbers under test
-#'
+#' @param system_list - dataframe containing a table with magic numbers used by robots. Stored in file Setup.csv
+#' @param path_data - string, path to the folder where optimization file should be written
 #' @param num_trades_to_consider - Number of trades to calculate profit factor
-#' @param profit_factor_limit - Limit below which trading robot considered not working properly
-#' @param demo_mode - When true function uses package test dataset
+#' @param profit_factor_limit - Limit below which trading robot is considered not working properly
 #' @param write_mode - When true function will write result to the file located in the temporary directory
 #'
 #' @return function returns a dataframe with systems that should be optimized
 #'
-#' @author (C) 2019 Vladimir Zhbanko
+#' @author (C) 2019,2020 Vladimir Zhbanko
 #'
 #' @export
 #'
 #' @examples
 #'
 #' library(lazytrade)
+#' library(magrittr)
 #' library(dplyr)
 #' library(readr)
 #' library(lubridate)
-#' DFT1 <- import_data(trade_log_file = system.file("extdata",
-#'                                                  "OrdersResultsT1.csv",
-#'                                                  package = "lazytrade"),
-#'                     demo_mode = TRUE)
 #'
+#' path_data <- normalizePath(tempdir(),winslash = "/")
 #'
-#' # without writing to the file
-#' DFT1 %>% check_if_optimize(num_trades_to_consider = 3,
-#'                            profit_factor_limit = 1.2,
-#'                            demo_mode = TRUE,
-#'                            write_mode = FALSE)
+#' file.copy(from = system.file("extdata", "Setup.csv", package = "lazytrade"),
+#'           to = file.path(path_data, "Setup.csv"), overwrite = TRUE)
+#'
+#' system_list <- read_csv(file.path(path_data, "Setup.csv"))
 #'
-#' # function will write to the temporary file
-#' DFT1 %>% check_if_optimize(num_trades_to_consider = 3,
-#'                            profit_factor_limit = 1.2,
-#'                            demo_mode = TRUE,
-#'                            write_mode = TRUE)
+#' data(profit_factorDF)
 #'
 #'
-check_if_optimize <- function(x, path_trading_robot = "",
+#' # without writing to the file
+#' check_if_optimize(x = profit_factorDF,
+#'                   system_list = system_list,
+#'                   path_data,
+#'                   num_trades_to_consider = 3,
+#'                   profit_factor_limit = 0.8,
+#'                   write_mode = TRUE)
+#'
+#'
+check_if_optimize <- function(x,
+                              system_list,
+                              path_data,
                               num_trades_to_consider = 3,
                               profit_factor_limit = 0.7,
-                              demo_mode = FALSE,
                               write_mode = FALSE){
 
   requireNamespace("dplyr", quietly = TRUE)
   requireNamespace("readr", quietly = TRUE)
 
 
-  if(!demo_mode){
-
   y <- x %>%  # filtered to contain last 20 orders for each system
+    dplyr::ungroup() %>%
         dplyr::group_by(MagicNumber) %>%
         dplyr::arrange(MagicNumber, desc(OrderCloseTime)) %>%
         dplyr::filter(row_number() <= num_trades_to_consider+1) %>%
@@ -69,30 +69,13 @@ check_if_optimize <- function(x, path_trading_robot = "",
         dplyr::filter(PrFact < profit_factor_limit) %>%
         dplyr::select(MagicNumber, PrFact) %>%
         dplyr::mutate(ToOptimize = 1) %>%
-        dplyr::inner_join(y = readr::read_csv(file = file.path(path_trading_robot,"TEST", "Setup.csv"),
-                                col_types = "cci"),
-                   by = c("MagicNumber" = "Magic"))
-  if(write_mode){
-        readr::write_csv(y, path = file.path(paste0(path_trading_robot, "TEST/", Sys.Date(), "-Re-Train", ".csv")))
-    } else {return(y)}
+        dplyr::inner_join(y = system_list, by = c("MagicNumber" = "Magic"))
 
-  } else {
+    if(write_mode){
+        readr::write_csv(y, path = file.path(path_data, paste0(Sys.Date(), "-Re-Train", ".csv")))
+    } else {return(y)}
 
-  y <- x %>%  # filtered to contain last 20 orders for each system
-        dplyr::group_by(MagicNumber) %>%
-        dplyr::arrange(MagicNumber, desc(OrderCloseTime)) %>%
-        dplyr::filter(row_number() <= num_trades_to_consider+1) %>%
-        lazytrade::get_profit_factorDF(num_trades_to_consider) %>%
-        dplyr::ungroup() %>%
-        dplyr::filter(PrFact < profit_factor_limit) %>%
-        dplyr::select(MagicNumber, PrFact) %>%
-        dplyr::mutate(ToOptimize = 1) %>%
-        dplyr::inner_join(y = readr::read_csv(file = system.file("extdata", "Setup.csv", package = "lazytrade"),
-                                col_types = "cci"),
-                   by = c("MagicNumber" = "Magic"))
-  if(write_mode){readr::write_csv(y, tempfile("Re-Train", fileext = ".csv"))} else {return(y)}
 
-  }
 
 
 }

R/create_labelled_data.R

@@ -26,6 +26,7 @@
 #'
 #'
 #' library(dplyr)
+#' library(magrittr)
 #' library(readr)
 #' library(lazytrade)
 #'
@@ -51,7 +52,7 @@ create_labelled_data <- function(x, n = 50, type = "regression"){
     # remove column 1 with data and time information
     dplyr::select(-1) %>%
     # split dataset into several objects each containing n rows (it will be a list)
-    split(rep(1:ceiling(nr/n), each=n, length.out=nr)) #list
+    base::split(rep(1:ceiling(nr/n), each=n, length.out=nr)) #list
   # remove last element of the list
   dat11[length(dat11)] <- NULL
 

R/datasets.R

@@ -252,7 +252,7 @@
 #'   \item{M_T}{Category of Market Type}
 #' }
 #'
-"macd_ML2"
+"macd_ML60M"
 
 #' Table with indicator only used to train model, 128 col 1646 rows
 #'

R/evaluate_macroeconomic_event.R

@@ -38,6 +38,7 @@
 #' # evaluate data on macroeconomic event (required to start trading)
 #' library(dplyr)
 #' library(readr)
+#' library(lazytrade)
 #'
 #' dir <- normalizePath(tempdir(),winslash = "/")
 #'
@@ -79,7 +80,7 @@ evaluate_macroeconomic_event <- function(setup_file_path,
     #read the file containing a flag (1 will mean that event is present hence no new opened orders)
     DF_NT <- readr::read_csv(file= macro_complete_path, col_types = "i")
     #read the table of trading robots in operation
-    DF_Setup <- readr::read_csv(setup_complete_path)
+    DF_Setup <- readr::read_csv(setup_complete_path, col_types = "ffi")
 
     ## condition to disable systems
     if(DF_NT[1,1] == 1) {

R/get_profit_factorDF.R

@@ -14,7 +14,8 @@
 #' library(dplyr)
 #' library(magrittr)
 #' data(profit_factorDF)
-#' get_profit_factorDF(profit_factorDF, 10)
+#' get_profit_factorDF(x = profit_factorDF,
+#'                     num_orders = 10)
 #'
 #'
 #'
@@ -25,35 +26,36 @@ get_profit_factorDF <- function(x, num_orders){
   x <- x %>% ungroup() #ungroup to remove warning
 
   DF_L <- x %>%
-    group_by(MagicNumber) %>%
-    summarise(nOrders = n())%>%
-    filter(nOrders > num_orders)%>%
-    select(MagicNumber)%>%
+    dplyr::group_by(MagicNumber) %>%
+    dplyr::summarise(nOrders = n(), .groups = "drop_last") %>%
+
+    dplyr::filter(nOrders > num_orders) %>%
+    dplyr::select(MagicNumber) %>%
     # subset only rows that contans magic numbers from x
-    inner_join(x, by = "MagicNumber")%>%
-    group_by(MagicNumber)%>%
-    filter(Profit < 0) %>%
-    summarise(Loss = abs(sum(Profit)))
+    dplyr::inner_join(x, by = "MagicNumber") %>%
+    dplyr::group_by(MagicNumber) %>%
+    dplyr::filter(Profit < 0) %>%
+    dplyr::summarise(Loss = abs(sum(Profit)), .groups = "drop_last")
   # generate DF with only MagicNumbers when > 10 trades and all trades are profits
   DF_P <- x %>%
-    group_by(MagicNumber) %>%
-    summarise(nOrders = n())%>%
-    filter(nOrders > num_orders)%>%
-    select(MagicNumber)%>%
+    dplyr::group_by(MagicNumber) %>%
+    dplyr::summarise(nOrders = n(), .groups = "drop_last") %>%
+    dplyr::filter(nOrders > num_orders)%>%
+    dplyr::select(MagicNumber)%>%
     # subset only rows that contans magic numbers from x
-    inner_join(x, by = "MagicNumber")%>%
-    group_by(MagicNumber)%>%
-    filter(Profit > 0) %>%
-    summarise(Gain = abs(sum(Profit)))
+    dplyr::inner_join(x, by = "MagicNumber")%>%
+    dplyr::group_by(MagicNumber)%>%
+    dplyr::filter(Profit > 0) %>%
+    dplyr::summarise(Gain = abs(sum(Profit)), .groups = "drop_last")
   # generate DF containing profit factor of all systems
   DF <- DF_P %>%
-    full_join(DF_L, by = "MagicNumber")
+    dplyr::full_join(DF_L, by = "MagicNumber")
   # replace any NA with 1!
   DF[is.na(DF)] <- 1
   # calculate profit factors of the each system!
   DF_PF <- DF%>%
-    group_by(MagicNumber)%>%
-    mutate(PrFact = Gain/(0.001+Loss))%>%
-    select(MagicNumber, PrFact)
+    dplyr::group_by(MagicNumber)%>%
+    dplyr::mutate(PrFact = Gain/(0.001+Loss))%>%
+    dplyr::select(MagicNumber, PrFact)
   return(DF_PF)
 }