pmenv 1.0.1

A portfolio rebalancing task environment for reinforcement learning

pmenv

Introduction to the pmenv Package

The pmenv is a reinforcement learning environment for the Portfolio Rebalancing Task. It loads multiple stock market data using tickers and synchronizes them into a 3D tensor format suitable for learning. It is an efficient environment for implementing and testing various reinforcement learning algorithms.

Overview

The pmenv is composed of two modules, DataManager and Environment:

• The Environment module plays a role in simulating the stock market environment and handling the transitions caused by price changes and transactions. The observation is a vector of Open, High, Low, Close price and Volume and the action space is $[-1,1]^N$ where $N$ is a number of risky assets in the portfolio. Portfolio is a vector of weights of all assets and the first entry always represents cash weight.

• The DataManager module is responsible for loading multiple stock market data using tickers and synchronizing them into a unified format suitable for learning.

The mathematical details of the environment are implemented based on the following paper. Please refer to the paper for further information. An intelligent financial portfolio trading strategy using deep Q-learning, ESWA, 2020

Install

pip install pmenv

Basic Usage

import pmenv

datamanager = pmenv.DataManager()

# Tickers and Period
tickers = ["AAPL", "INCY", "REGN"]

train_start = "2019-01-02"
train_end = "2021-06-30"

# Load stock OHLCV dataframe with a ticker
data = datamanager.get_data(tickers[0], train_start, train_end)
# Load stock data tensor with ticker list
data_tensor = datamanager.get_data_tensor(tickers, train_start, train_end)


environment = pmenv.Environment(data_tensor)

# Set transaction cost
config = {"CHARGE": 0.0025, "TEX": 0.001}
environment.config = config

# Initialize the environment with balance
balance = 5000
observation = environment.reset(balance)

# Step
action = np.random.random(size=(len(tickers))
next_observation, reward, done = env.step(action)

Tutorial with RL

1. Portfolio Rebalancing with Deep Q-Learning
2. Portfolio Rebalancing with DDPG
3. Portfolio Rebalancing with Dirichlet Distribution Trader