lazytrade (version 0.4.4)

mt_stat_evaluate: Function to prepare and score data, finally predict current market type using pre-trained classification model

Description

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

Usage

mt_stat_evaluate(x, path_model, num_bars, timeframe)

Arguments

x

- dataframe with one column containing asset indicator in the time descending order, typically 64 or more values

path_model

String, path to the model

num_bars

Integer, Number of bars used to perform transformation

timeframe

Integer, timeframe in Minutes.

Value

dataframe with predicted value of the market type

Details

it is mandatory to switch on the virtual h2o machine with h2o.init() also to shut it down with h2o.shutdown(prompt = F)

Examples

# NOT RUN {
# }
# NOT RUN {
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,
              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)

# }
# NOT RUN {
# }