mt_evaluate | R Documentation |
PURPOSE: Function that uses Deep Learning model and Time Series Column of the dataframe to find out specific market type of the financial asset it will also discard bad result outputting -1 if it is the case
mt_evaluate(x, path_model, num_cols, timeframe)
x |
|
path_model |
String, path to the model |
num_cols |
Integer, number of columns (features) in the final vector input to the model |
timeframe |
Integer, timeframe in Minutes. |
it is mandatory to switch on the virtual h2o machine with h2o.init() also to shut it down with h2o.shutdown(prompt = F)
dataframe with predicted value of the market type
library(h2o)
library(magrittr)
library(dplyr)
library(readr)
library(lazytrade)
path_model <- normalizePath(tempdir(),winslash = "/")
path_data <- normalizePath(tempdir(),winslash = "/")
data(macd_ML60M)
# start h2o engine (using all CPU's by default)
h2o.init(nthreads = 2)
# performing Deep Learning Regression using the custom function
# this function stores model to the temp location
mt_make_model(indicator_dataset = macd_ML60M,
num_bars = 64,
timeframe = 60,
path_model = path_model,
path_data = path_data,
activate_balance = TRUE,
num_nn_options = 3)
# Use sample data
data(macd_100)
# use one column for testing
x <- macd_100[ ,2]
mt_evaluate(x = x,
path_model = path_model,
num_cols = 64,
timeframe = 60)
h2o.shutdown(prompt = FALSE)
#set delay to insure h2o unit closes properly before the next test
Sys.sleep(5)
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