Description Usage Arguments Value Author(s) Examples
View source: R/mt_stat_evaluate.R
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
1 | mt_stat_evaluate(x, path_model, num_bars, timeframe)
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x |
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path_model |
String, path to the model |
num_bars |
Integer, Number of bars used to perform transformation |
timeframe |
Integer, timeframe in Minutes. |
dataframe with predicted value of the market type
(C) 2021 Vladimir Zhbanko
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | library(h2o)
library(magrittr)
library(dplyr)
library(readr)
library(lazytrade)
library(stats)
path_model <- normalizePath(tempdir(),winslash = "/")
path_data <- normalizePath(tempdir(),winslash = "/")
# start h2o engine (using all CPU's by default)
h2o.init(nthreads = 2)
data(price_dataset_big)
data <- head(price_dataset_big, 500) #reduce computational time
ai_class <- mt_stat_transf(indicator_dataset = data,
num_bars = 64,
timeframe = 60,
path_data = path_data,
mt_classes = c('BUN', 'BEN', 'RAN'))
# performing Deep Learning Classification using the custom function auto clustered data
mt_make_model(indicator_dataset = ai_class,
num_bars = 64,
timeframe = 60,
path_model = path_model,
path_data = path_data,
activate_balance = TRUE,
num_nn_options = 3,
num_epoch = 10,
is_cluster = TRUE)
# Use sample data
data(price_dataset)
# use one column for testing
x <- price_dataset[ ,2]
mt_stat_evaluate(x = x,
path_model = path_model,
num_bars = 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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