Deep Reinforcement Learning on a Multi-Asset Environment for Trading

@article{Hirsa2021DeepRL,
  title={Deep Reinforcement Learning on a Multi-Asset Environment for Trading},
  author={Ali Hirsa and Branka Hadji Misheva and Joerg Osterrieder and Jan-Alexander Posth},
  journal={International Political Economy: Investment \& Finance eJournal},
  year={2021}
}
Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorated method with significant success in multiple domains, still has to show its benefit in the financial markets. We use a deep Q-network (DQN) to design long-short trading strategies for futures contracts. The state space consists of volatility-normalized daily returns, with… 
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References

SHOWING 1-10 OF 14 REFERENCES

Financial Trading as a Game: A Deep Reinforcement Learning Approach

TLDR
An Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm and develops an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent.

Deep Reinforcement Learning for Trading

TLDR
The experiments show that the proposed algorithms can follow large market trends without changing positions and can also scale down, or hold, through consolidation periods, and are equivalent if a linear utility function is used.

Practical Deep Reinforcement Learning Approach for Stock Trading

TLDR
The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

TLDR
This work introduces a recurrent deep neural network for real-time financial signal representation and trading and proposes a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training.

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

TLDR
A financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem, able to achieve at least 4-fold returns in 50 days.

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

TLDR
Backtesting on a portfolio of 88 continuous futures contracts, it is demonstrated that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points.

Optimal Asset Allocation using Adaptive Dynamic Programming

TLDR
Asset allocation is formalized as a Markovian Decision Problem which can be optimized by applying dynamic programming or reinforcement learning based algorithms and is shown to be equivalent to a policy computed by dynamic programming.

The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets

TLDR
This study shows enormous potential of applying self-play concepts and algorithms to financial markets and economic forecasts, and shows that academic research using Self-Play has mostly not focused on finance, and if it has, it was usually restricted to stock markets, not considering the large FX, commodities and bond markets.

Portfolio Management using Reinforcement Learning

In this project, we use deep Q-learning to train a neural network to manage a stock portfolio of two stocks. In most cases the neural networks performed on par with benchmarks, although some models

Handling risk-on/risk-off dynamics with correlation regimes and correlation networks

TLDR
This paper presents a framework for detecting distinct correlation regimes and analyzing the emerging state dependences for a multi-asset futures portfolio from 1998 to 2013 and quantifies these observations using suitable metrics for the clusters and correlation networks.