rl_generate_policy_mt: Function performs RL and generates model policy for each...

In lazytrade: Learn Computer and Data Science using Algorithmic Trading

Description

This function will perform Reinforcement Learning using Trading Data. It will suggest whether or not it is better to keep using trading systems or not. Function is just using results of the past performance to generate the recommendation (not a holy grail).

Usage

rl_generate_policy_mt(x, states, actions, control)

Arguments

x	Dataframe containing trading data
states	Character vector, Selected states of the System
actions	Character vector, Selected actions executed under environment
control	List, control parameters as defined in the Reinforcement Learning Package

Details

Initial policy is generated using a dummy zero values. This way function starts working directly from the first observation. However policy 'ON' value will only be generated once the Q value is greater than zero

Value

Function returns data frame with reinforcement learning model policy

Examples

library(dplyr)
library(magrittr)
library(ReinforcementLearning)
library(lazytrade)
data(trading_systemDF)
states <- c("BUN", "BUV", "BEN", "BEV", "RAN", "RAV")
actions <- c("ON", "OFF")
control <- list(alpha = 0.7, gamma = 0.3, epsilon = 0.1)
rl_generate_policy_mt(x = trading_systemDF,
                      states = states,
                      actions = actions,
                      control = control)

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