rl_log_progress_mt: Function to retrieve and help to log Q values during RL...

In lazytrade: Learn Computer and Data Science using Algorithmic Trading

Description

Function will record Q values during the model update. These values will be used by another function Function was developed to help to estimate best control parameters during optimisation process

Usage

rl_log_progress_mt(x, states, actions, control)

Arguments

x	dataframe containing trading results
states	Selected states of the System
actions	Selected actions executed under environment
control	control parameters as defined in the Reinforcement Learning Package

Value

dataframe with log of RL model reward sequences during model update

Author(s)

(C) 2020, 2021 Vladimir Zhbanko

Examples

# retrieve RL model Q values progress
library(ReinforcementLearning)
library(dplyr)
library(magrittr)
library(lazytrade)
data(trading_systemDF)
x <- trading_systemDF
states <- c("BUN", "BUV", "BEN", "BEV", "RAN", "RAV")
actions <- c("ON", "OFF") # 'ON' and 'OFF' are referring to decision to trade with Slave system
control <- list(alpha = 0.7, gamma = 0.3, epsilon = 0.1)

rl_log_progress_mt(x = x,states = states, actions = actions, control = control)

lazytrade documentation built on Dec. 16, 2021, 1:06 a.m.