lazytrade (version 0.4.0)

aml_score_data: Function to score new data and predict change for each single currency pair

Description

Function is using the latest data from the financial assets indicator pattern and deep learning model. Prediction is a price change in the future for that asset will be used by the trading system

Usage

aml_score_data(symbol, timeframe, path_model, path_data, path_sbxm, path_sbxs)

Arguments

symbol

Character symbol of the asset for which the model shall predict

timeframe

Data timeframe e.g. 60 min

path_model

Path where the models are be stored

path_data

Path where the aggregated historical data is stored, if exists in rds format

path_sbxm

Path to the sandbox where file with predicted price should be written (master terminal)

path_sbxs

Path to the sandbox where file with predicted price should be written (slave terminal)

Value

Function is writing file into Decision Support System folder, mainly file with price change prediction in pips

Details

Performs fresh data reading from the rds file

Examples

# NOT RUN {
# }
# NOT RUN {
# test of function aml_make_model is duplicated here
library(dplyr)
library(readr)
library(lubridate)
library(h2o)
library(magrittr)
library(lazytrade)

path_model <- normalizePath(tempdir(),winslash = "/")
path_data <- normalizePath(tempdir(),winslash = "/")

ind = system.file("extdata", "AI_RSIADXUSDJPY60.csv",
                  package = "lazytrade") %>% read_csv(col_names = FALSE)

ind$X1 <- ymd_hms(ind$X1)

write_csv(ind, file.path(path_data, "AI_RSIADXUSDJPY60.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)
# start h2o engine (using all CPU's by default)

h2o.init()


# performing Deep Learning Regression using the custom function
aml_make_model(symbol = 'USDJPY',
               timeframe = 60,
               path_model = path_model,
               path_data = path_data)


path_sbxm <- normalizePath(tempdir(),winslash = "/")
path_sbxs <- normalizePath(tempdir(),winslash = "/")


# score the latest data to generate predictions for one currency pair
aml_score_data(symbol = 'USDJPY',
               timeframe = 60,
               path_model = path_model,
               path_data = path_data,
               path_sbxm = path_sbxm,
               path_sbxs = path_sbxs)

# stop h2o engine
h2o.shutdown(prompt = FALSE)

#set delay to insure h2o unit closes properly before the next test
Sys.sleep(5)

# }
# NOT RUN {


# }

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