rl_generate_policy: Function performs Reinforcement Learning using the past data...

In lazytrade: Learn Computer and Data Science using Algorithmic Trading

Function performs Reinforcement Learning using the past data to generate model policy

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(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

Author(s)

(C) 2019,2021 Vladimir Zhbanko

Examples

  library(dplyr)
  library(ReinforcementLearning)
  library(magrittr)
  library(lazytrade)

  data(data_trades)
  states <- c("tradewin", "tradeloss")
  actions <- c("ON", "OFF")
  control <- list(alpha = 0.7, gamma = 0.3, epsilon = 0.1)
  rl_generate_policy(x = data_trades,
                     states, actions, control)

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