mt_evaluate: Function to score data and predict current market type using...

In lazytrade: Learn Computer and Data Science using Algorithmic Trading

Function to score data and 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_evaluate(x, path_model, num_cols, 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_cols	Integer, number of columns (features) in the final vector input to the model
timeframe	Integer, timeframe in Minutes.

Details

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

Value

dataframe with predicted value of the market type

Examples

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)

lazytrade documentation built on Sept. 12, 2024, 9:36 a.m.