mt_stat_evaluate {lazytrade} | R Documentation |
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 |
|
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
Author(s)
(C) 2021 Vladimir Zhbanko
Examples
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)
[Package lazytrade version 0.5.4 Index]