mt_stat_evaluate: Function to prepare and score data, finally predict current...

In vzhomeexperiments/lazytrade: Learn Computer and Data Science using Algorithmic Trading

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

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)

vzhomeexperiments/lazytrade documentation built on Feb. 20, 2024, 6:09 p.m.