Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks

By David W. Lu. 2017

52 citations recorded by the source corpus.

With the breakthrough of computational power and deep neural networks, many areas that we haven't explore with various techniques that was researched rigorously in past is feasible. In this paper, we will walk through possible concepts to achieve robo-like trading or advising. In order to accomplish similar level of performance and generality, like a human trader, our agents learn for themselves to create successful strategies that lead to the human-level long-term rewards. The learning model is implemented in Long Short Term Memory (LSTM) recurrent structures with Reinforcement Learning or Evolution Strategies acting as agents The robustness and feasibility of the system is verified on GBPUSD trading.

Subfields: Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. H. Buhler
2. Kangkang Jiang
3. Ben Wood
4. Yanran Li
5. Junhao Zhu
6. Hao Chen
7. Josef Teichmann
8. Zhipeng Liang

In Management Science and Operations Research

1. H. Buhler
2. Kangkang Jiang
3. Ben Wood
4. Yanran Li
5. Junhao Zhu
6. Hao Chen
7. Josef Teichmann
8. Zhipeng Liang

Citations in the corpus

• Adversarial Deep Reinforcement Learning in Portfolio Management (2018, methodology): “Lu implemented in Long Short Term Memory (LSTM) recurrent structures with Reinforcement Learning or Evolution Strategies acting as agents The robustness and feasibility of the system is verified on GBPUSD trading [14].”
• Deep hedging (2018, result): “The second promising application of reinforcement learning is in algorithmic trading, where several authors have shown promising results, e.g. [DZL09] and [Lu17] to give but two examples.”