A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

Code

• github.com/ZhengyaoJiang/PGPortfolio
  Officialtf★ 0
• github.com/andreaslillevangbech/PortfolioManager-pytorch
  pytorch★ 15
• github.com/aleedelarica/XDRL-for-finance
  tf★ 5
• github.com/jackieli19/pgportfolio
  tf★ 3
• github.com/cove9988/TradingGym
  none★ 0
• github.com/jadag/trader
  tf★ 0
• github.com/5410tiffany/EIIE-keras
  tf★ 0
• github.com/collinarnett/safex_trading_bot
  pytorch★ 0
• github.com/gbotev/PGPortfolio
  tf★ 0
• github.com/collinarnett/xcalibra_trading_bot
  pytorch★ 0

Abstract

Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 50 days.

Tasks

Reproductions