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

In lazytrade: Learn Computer and Data Science using Algorithmic Trading

Description

Function is using the latest data from the financial assets indicator pattern and deep learning model. Prediction is a future price change for that asset

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)

Details

Performs fresh data reading from the rds file

Value

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

Author(s)

(C) 2020 Vladimir Zhbanko

Examples

# 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)

# 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)
# start h2o engine (using all CPU's by default)

h2o.init(nthreads = 2)


# performing Deep Learning Regression using the custom function
aml_make_model(symbol = 'USDJPY',
               timeframe = 60,
               path_model = path_model,
               path_data = path_data,
               force_update=FALSE,
               num_nn_options = 2)


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)

lazytrade documentation built on Dec. 16, 2021, 1:06 a.m.