View source: R/aml_collect_data.R
aml_collect_data | R Documentation |
Function is collecting data from the csv files Data objects are transformed to be suitable for Regression Modelling. Price change will be in the column 'LABEL', column X1 will keep the time index Result will be written to a new or aggregated to the existing '.rds' file
Function is keeping generated dataset to be not larger than specified by the user
aml_collect_data(
indicator_dataset,
symbol,
timeframe = 60,
path_data,
max_nrows = 2500
)
indicator_dataset |
Dataset containing assets indicator which pattern will be used as predictor |
symbol |
Character symbol of the asset for which to train the model |
timeframe |
Data timeframe e.g. 1 min |
path_data |
Path where the aggregated historical data is stored, if exists in rds format |
max_nrows |
Integer, Maximum number of rows to collect |
Function is not handling shift of the price and indicator datasets.
This function is relying on the data collection from the dedicated data robot Other 'aml_*' functions will work based on the data processed by this function
Function is writing files into Decision Support System folder, mainly file object with the model
(C) 2020, 2021 Vladimir Zhbanko
# write examples for the function
library(dplyr)
library(readr)
library(lubridate)
library(lazytrade)
library(magrittr)
# sample dataset
ind = system.file("extdata", "AI_RSIADXUSDJPY60.csv",
package = "lazytrade") %>% read_csv(col_names = FALSE)
# convert to POSIX format
ind$X1 <- ymd_hms(ind$X1)
# create temporary path (check output of tempdir() to check the result)
path_data <- normalizePath(tempdir(),winslash = "/")
# add tick data to the folder
tick = system.file("extdata", "TickSize_AI_RSIADX.csv",
package = "lazytrade") %>% read_csv(col_names = FALSE)
write_csv(tick, file.path(path_data, "TickSize_AI_RSIADX.csv"), col_names = FALSE)
# data transformation using the custom function for one symbol
aml_collect_data(indicator_dataset = ind,
symbol = 'USDJPY',
timeframe = 60,
path_data = path_data)
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